Handbook
for
Integrating Poverty
Impact Assessment
in the
Economic Analysis
of Projects
Economics and Development Resource Center
(EDRC)
July 2001
Handbook Poverty Impact Prelims.p65
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© Asian Development Bank 2001
All rights reserved
ISBN 971-561-285-7
Publication Stock No. 020100
Published by the Asian Development Bank
P.O. Box 789, 0980 Manila, Philippines
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Foreword
T
his Handbook is the second output of EDRC’s study on integrating
poverty impact assessment in the economic analysis of projects. Manabu
Fujimura led the study under the overall supervision and guidance of
David Edwards, then Assistant Chief Economist. John Weiss (Staff Consultant)
provided technical inputs and advice throughout the study. Nigel Rayner
contributed Appendix 2. Olivier Dupriez and Christopher Edmonds contributed
Appendix 3. Tumurdavaa Bayarsaihan contributed Case 2 in Appendix 6. Case
4 in Appendix 6 benefited from the poverty analysis included in Nanak
Kakwani’s (Staff Consultant) work. Marcelia Garcia and Ma. Virginita Capulong
provided assistance for literature search. Anneli Lagman did a consistency
check on the technical presentation of the final draft. Regina Sibal contributed
the glossary of terms and was the sole undertaker of word processing and
graphics. Lily Bernal did the proofreading.
Preparation of the Handbook underwent three stages. In the first stage,
a draft issues paper was prepared and received interdepartmental comments,
which resulted in the EDRC paper “Integration of Poverty Impact in Project
Economic Analysis: Issues in Theory and Practice.” In the second stage, a
preliminary draft of the Handbook was presented in an EDRC seminar, which
enhanced interactions with operational departments. Sean O’Sullivan and Bo
Lin shared their work on ADB’s power projects. Stephen Curry, Francesca
Agnello, Jeffrey Miller, and Hiromi Sakurai shared their recent work on ADB’s
transport projects. In the third stage, the final draft of the Handbook was
circulated and received detailed comments. The Handbook benefited
particularly from comments and suggestions provided by Stephen Curry, Rita
Nangia, Etienne Van de Walle, Adrian Ruthenberg, Mandar Jayawant, Tyrell
Duncan, Lourdes Adriano, Sultan Hafeez Rahman, Alfredo Perdiguero, and
Cindy Houser. Arvind Panagariya, Chief Economist, and Xianbin Yao, Assistant
Chief Economist, provided guidance in the final stage.
The Handbook will serve as a reference material in preparing projects
under ADB’s renewed mandate of poverty reduction and augments the current
practice of project economic analysis. It illustrates applications of Appendixes
25 (Distribution Analysis) and 26 (Poverty Impact Analysis) of ADB’s Guidelines
for the Economic Analysis of Projects (1997). It is primarily intended for the
guidance of ADB staff and officials of its developing member countries, but it
may also be of interest to others involved in development assistance.
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iv
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Abbreviations and Acronyms
ADB
CBO
CPI
CWIQ
DALY
DHS
DMC
EDRC
EIRR
FIRR
GDP
HIES
ILO
ISA
Lao PDR
LECS
LSMS
MICS
MOLISA
NGO
NPV
OPEC
PIA
PIR
PPTA
RMS
RRP
RSA
SCF
SERF
SWRF
TA
TLSS
VOC
Asian Development Bank
community-based organization
consumer price index
Core Welfare Indicators Questionnaire
disability adjusted life years
demographic and health survey
developing member country
Economics and Development Resource Center
economic internal rate of return
financial internal rate of return
gross domestic product
household income and expenditure survey
International Labuor Organisation
initial social assessment
Lao People’s Democratic Republic
Lao Expenditure and Consumption Survey
living standards measurement survey
multiple indicator cluster survey
Ministry of Labor, Invalids and Social Affairs, Viet Nam
nongovernment organization
net present value
Organization of Petroleum Exporting Countries
poverty impact assessment
poverty impact ratio
project/program preparatory technical assistance
rapid monitoring survey
report and recommendation of the President
rapid social assessment
standard conversion factor
shadow exchange rate factor
shadow wage rate factor
technical assistance
Tajikistan Living Standards Survey
vehicle operating cost
Note: In this Handbook, “$” refers to US dollars.
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1
v
Introduction
Contents
Page
For
ewor
d
orewor
eword
iii
Abbreviations
iv
1
INTRODUCTION
1
2
PRO-POOR CONTEXT CHECK FOR OTHER
INTER
VENTION PROJECTS
INTERVENTION
5
3
4
APPRO
ACH TO PO
VER
TY IMP
ACT ANAL
YSIS FOR
APPROA
POVER
VERTY
IMPA
ANALYSIS
PO
VER
TY INTER
VENTION PROJECTS
POVER
VERTY
INTERVENTION
Pre-PPTA Stage
PPTA Stage
Steps for Poverty Impact Analysis
9
12
13
14
DATA REQUIREMENTS
Benefit Incidence
Government Net Benefit and Effects on the Rest of the
Economy
17
18
5
AD
VANT
AGES AND LIMIT
ATIONS OF THE ANAL
YSIS
ADV
ANTA
LIMITA
ANALYSIS
Improving Project Quality at Entry
Complementing the Inadequacy of Headcount Approach
Variety of Projects
Caution on Interpretation of PIR index
Risk for the Poor
23
23
24
26
27
27
6
LINK
AGE TO POLIC
Y-BASED LENDING
LINKA
POLICY
29
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
APPENDIXES
1.
Distribution and Poverty Impact Analysis (reproduced from
ADB Guidelines for the Economic Analysis of Projects)
2.
Benchmark Criteria for Good Practice Project Preparation
3.
Application of Existing Primary Survey Data in Poverty Impact
Analysis at the Project Level
4.
Case Illustrations of Distribution Analysis
Case 1: Mongolia – Energy Conservation Project
Case 2: Bangladesh – Jamuna Bridge Project
5.
Incorporating Project Financing in Distribution and Poverty
Impact Analysis
6.
Case Illustrations of Poverty Impact Analysis
Case 1: Philippines – Transmission Interconnection and
Reinforcement Project
Case 2: Viet Nam – Second Red River Basin Water
Resource Project
Case 3: Sri Lanka – Coastal Resource
Management Project
Case 4: Lao PDR – Primary Health Care
Expansion Project
Case 5: Tajikistan – Road Rehabilitation Project
7.
Approximation of Income Share of the Poor
8.
Sample Terms of Reference and Resource Requirements
121
128
137
141
GLOSSAR
Y
GLOSSARY
145
REFERENCES
151
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31
33
43
45
67
67
74
83
89
90
98
109
1
Introduction
DB's adoption of poverty reduction as its overarching objective means
that all its interventions must be prepared with poverty impact as the
primary focus in one form or another. ADB's Poverty Reduction Strategy
(October 1999) appropriately sets out the three pillars of poverty reduction:
pro-poor sustainable economic growth, social development, and good
governance. However, it should be recognized that existing economic theory
and cross-country empirical evidence are not likely to be a satisfactory guide
in terms of informing policy options for individual developing member countries
(DMCs) of ADB toward maximum poverty reduction. Pro-poor growth that is
favored by most international agencies including ADB can be interpreted as
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
an outcome from a mix of interventions, but identification of such mix is a
complex undertaking in practice. A challenging operational task for ADB in
the short to medium term is to find a good balance between the two
intervention categories adopted: Poverty Interventions (of which Core Poverty
Interventions are a subset) and the other interventions (Loan Classification
System, November 2000). (This Handbook will refer to the second group simply
as Other Interventions throughout.) It can be broadly considered that Poverty
Interventions will be targeted projects while Other Interventions will be nontargeted projects. ADB's Poverty Reduction Strategy (paragraphs 55-56)
recognizes that "it is often difficult in the case of individual countries to decide
how much emphasis to place on poverty interventions and how much on
growth-oriented investments. Where past performance in poverty reduction
has been weak or inequality is rising, the emphasis will be on governance
and social development. In countries where essential reforms have been
undertaken or are under way, growth-oriented investments will reduce
poverty…In each country, the mix and nature of projects will be shaped by
the poverty analysis (to be carried out for each DMC)."
As an intermediate step before country-specific poverty analysis can
better inform the appropriate intervention mix for poverty reduction, one could
think of some country grouping with which to broadly guide ADB's country
programming. For example, DMCs with demonstrated growth performance
but potential or alarming pace of increasing inequality and poor record of
poverty reduction might need more poverty interventions than growthoriented projects. DMCs with relatively equitable institutional setup but weak
growth performance might need more growth-oriented projects than poverty
interventions. DMCs with less than mediocre record on both growth and
equitable institution and governance in general, including the economies in
transition, might need governance reform interventions before anything else
as a prerequisite to the possibility of poverty-reducing growth process. However,
truly useful information must come from country-specific studies. Some earlier
country-specific poverty studies undertaken by ADB include Quibria (1993,
1994) and Pernia (1994) covering Bangladesh, India, Sri Lanka, Indonesia,
Republic of Korea, Philippines, and Thailand. More knowledge at country
programming level must be accumulated over time to inform the exercise of
country strategy and program through economic and sector work (ESW).
This Handbook is intended as a reference material to assist ADB project
preparation in light of the economic analysis under the mandate of poverty
reduction. It provides workable recommendations for augmenting the current
practice. In accordance with the twofold loan classification, two separate
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Introduction
3
treatments are recommended. The main part of the Handbook (Chapters 3-5) is
devoted to the treatment of Poverty Intervention projects. It can be considered
as a detailed application of the ADB Guidelines for the Economic Analysis of
Projects (reproduced in Appendix 1). Treatment of Other Intervention projects
is discussed in Chapter 2. It should be noted that due to the specific focus on
the poverty impact of projects, this Handbook does not cover directly other
equally important aspects of project appraisal such as financial sustainability,
which needs continued attention. For general guidance on overall project
analysis, readers are referred to the Guidelines for the Economic Analysis of
Projects (ADB 1997).
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2
Pro-Poor
Context Check for
Other Intervention
Projects
arious policy and institutional contexts of project investments must be
informed by country- and sector-level knowledge based on
macroeconomic performance record, public expenditure review,
governance review, etc. In light of ADB’s poverty reduction objective, the
analyst should first check whether the project-induced growth effects will
lead to poverty reduction, e.g., with at least one-to-one relationship. If such
environment can be confirmed, a reasonable strategy would be to look for
investments with maximum growth impact, provided that care is taken to
ensure obvious negative externalities will not arise. A presumed pro-poor
rationale for Other Intervention (primarily nontargeted) projects must be that
their policy and institutional contexts are such that growth is the most effective
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
channel for poverty reduction. If, for example, social service delivery (e.g.,
primary education, primary health expenditure) and transfer mechanisms
(social safety net) in a project's target area(s) is demonstrated to be pro-poor,
there is less need to design the project in a pro-poor way especially where
such design tends to narrow the room for growth impact, which in turn narrow
the scope for government social expenditure targeted for the poor. Under
such environment, selection of projects with maximum net present value (NPV)
with a 12 percent cutoff economic internal rate of return (EIRR) can continue
to be the primary economic criteria.
In determining whether a project in question fits in such environment,
the analyst may proceed with a two-tier context check. The first-tier check
will be at economy-wide level, and the second-tier check will be at
geographically disaggregated local level. Much of the information required
in the first-tier check should be covered in country-level studies at CSP level.
Such information can be broadly categorized into the following:
Macroeconomic context: It is generally accepted that inflation has an
adverse impact on the poor involved in market economy as it works as a
regressive tax due to loss in what little purchasing power they have. Monetary
authorities cannot necessarily control the size and nature of the impact of
macroeconomic policy changes on the poor. Reconciliation of short-run and
long-run effects on the poor will not be straightforward. It may be best to
assign some macro policies to a limited policy goal such as controlled inflation
and fiscal stabilization. The analyst should at least check whether
macroeconomic management is broadly sound in this context.
Public expenditure and tax incidence on the poor: The public spending
incidence is often used as a shortcut to welfare measurement of public services.
While this is certainly an approximation of true benefit incidence, where data
or resources are not available for full-fledged behavioral analysis, conventional
benefit incidence results would be informative in judging the first-cut
redistributive environment of public spending. Methodological problems with
this approach pointed out, for example, in van de Walle (1996), may not be
a serious concern if its use is limited to checking a broad public spending
tendency instead of using it to guide actual public spending decisions. Tax
incidence analysis is the other side of the public expenditure and can be
applied in combination with public spending analysis. [See, for example, the
public spending chapter in the World Bank's Poverty Reduction Strategy Sourcebook (World Bank 2000d.)]
Institutional/governance context: The way rules of the game are written
and effectively enforced determines the overall parameters within which
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Pro-Poor Context Check for Other Intervention Projects
7
economic activities bring about growth and equity outcomes. While many
agencies have developed governance indicators and checklists, they could
be broadly categorized into two groups in terms of poverty impact: povertyneutral indicators and proactive indicators. The former group could include
accountable and contestable government, credibility of budget process, efficacy
of legal institutions, factor market efficiency, anticorruption legal framework
and enforcement, etc. The latter group could include social stratification/class
system, voice of the poor, asset distribution, credibility of social and
environmental protection; social safety net system, etc.
In line with the stipulation in the Advisory Notes on Poverty Analysis
(SPD April 2000), it is desirable that as much as possible of the following
information be covered in country poverty analysis:
(i) macroeconomic stability – inflation rate and its trend; exchange
rate depreciation trend and its impact on rural and urban poor;
(ii) asset distribution (especially landownership profile), preferably
with geographical breakdown, and its implication on the poor's
capability to participate in market activities;
(iii) labor market condition: market competitiveness; location and
density of labor-intensive industries and small and medium
interprises, etc., and their implication for employment of the poor;
(iv) public spending and tax incidence, preferably with geographical
breakdown (ideally covered by public expenditure review or social
expenditure review);
(v) government antipoverty programs: magnitude, location, sectors,
and types of antipoverty programs;
(vi) social safety net availability for the poor, preferably with
geographical breakdown (e.g., World Bank's social sector reviews,
International Labour Organisation’s social protection expenditure
reviews);
(vii) effectiveness of the regulatory regimes and implication on the
poor: e.g., existence and enforcement status of anticorruption laws
(ideally covered in country governance review);
(viii) indicators of risk-coping capacity of the poor, preferably with
geographical breakdown: social indicators such as education
levels and health status;
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
(ix) support of the civil society and private sector: existence of
nongovernment and community-based organizations etc., that
represent and promote the interests of the poor, with geographical
breakdown; and
(x) ongoing and planned external assistance: existence of targeted
poverty reduction initiatives with geographical breakdown.
In the second-tier context check, where information is inadequate at
disaggregate levels, the project analyst is responsible for collecting and
complementing the information specific to the local situation and examining
whether the project environment is conducive to the poor's access to the
services produced by the project.
It is expected that these information will be increasingly available
through ESW. In the meantime, the project analyst should rely on as much
existing evidence as meaningful to make a sensible judgement on the policy
and institutional context of the project at hand. Existing ADB documents for
program loans or sector development program loans with poverty impact
assessment (PIA) matrixes may include some clues to inform the project
environment. Where a series of ADB projects have been implemented within
a sector or subsector, evaluation reports may yield useful information on
governance performance over time and help examine its implication on the
poor.
A rigorous economic calculation of distribution and poverty impact
analysis, which follows in the next section, need not be essential for Other
Intervention projects. However, project economists and consultants are
encouraged to undertake such analyses wherever possible and meaningful
for many reasons (see discussion in Chapter 5). For benchmarks for good
project preparation with a poverty perspective, see Appendix 2.
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Introduction
9
3
Approach to
Poverty Impact
Analysis for Poverty
Intervention Projects
or Poverty Intervention (including Core Poverty Intervention) projects,
they should be subjected to an analysis specific to the poor beneficiaries
in addition to the conventional efficiency analysis represented by the EIRR
or aggregate NPV indicator. It would be ideal if a consistent yardstick could
be applied to rank all interventions. Economic logic requires that such yardstick
be based on the efficiency of delivering poverty reduction combined with a
weighting system that incorporates contributions to all other relevant
objectives, of which growth will be the most important. However, the practice
of necessity must fall short of this theoretical ideal. Apart from the intrinsic
problem of weighting different objectives there remains the methodological
difficulty of comparing interventions among (i) those with monetized net
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
benefits, (ii) those with quantifiable but nonmonetized net benefits, and (iii)
those with only beneficiary headcounts. Due to the diverse nature of the
interventions at ADB's disposal it is impractical to attempt to develop a
comprehensive methodology or a single criterion. Operational poverty impact
analysis cannot be expected to be any more standardized than the existing
practice of efficiency-based analysis. In addition there is a range of interventions
including policy-based lending and institutional strengthening that are not
easily amenable to rigorous quantitative methods, which is addressed in a
separate study undertaken by the Economics and Development Resource
Center (EDRC).
As early as in the late 1970s, ADB recognized the importance of bringing
in beneficiary identification and distributional impacts in project analysis (ADB
1978). There has been a recent attempt to promote a rigorous approach to
estimating poverty reduction impact of agricultural projects (Ali 1990). The
current ADB Guidelines (Appendix 1) introduces the methodology. But the
practice has not taken off on a regular basis until very recently. This Handbook
expands upon the Guidelines. It sets out briefly the main steps involved in
incorporating a poverty dimension into the economic analysis of projects,
identifies some of the main technical difficulties, and suggests some rules of
thumb as a practical means by which they can be overcome. The appendixes
discuss more specialist issues and illustrate the approach by an analysis of
case studies drawn from recent appraisals of ADB projects. Readers interested
in theoretical aspects and experiences of other international agencies in this
area are referred to Fujimura and Weiss (2000).
Economic analysis of projects uses a money-metric measure—i.e., as
far as possible all project effects should be expressed in terms of economic
benefits and costs expressed in monetary units. Hence it is logical that for the
purposes of project analysis, poverty should be defined also in income/
expenditure terms as opposed to headcount terms. This requires identifying
a poverty line level of income/expenditure and defining all those who fall
below this line as the poor and those whose income/expenditure is above it
as the nonpoor. For the purposes of defining poverty for ADB appraisals, it is
recommended that wherever possible the poverty line used should be a national
one agreed between ADB and the DMC government concerned.
Nonetheless, there may be circumstances in which a national poverty
line does not exist or where household income/expenditure data for project
beneficiaries are not available. Here it is sometimes possible to derive
approximate average income/expenditure data for aggregate groups of
beneficiaries, for example, by inferring income from data on household assets,
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Approach to Poverty Impact Analysis for Poverty Intervention Projects
11
such as hectares of land available for cultivation or adult family members
available for casual wage employment. Lack of access to key basic need items
—such as clean drinking water or primary education or to assets like types
of dwelling or animals—could be used as a means of defining which groups
fall into the category of the poor. The precise proxy to be applied would have
to be determined by the circumstances of the case (see Appendix 3 for available
survey data). The implication is not that this is a superior measure to income/
expenditure but that if households had access to these assets they would use
it to purchase the basic need concerned. As far as possible for consistency,
the lack of access to the same basic need indicator—such as clean drinking
water—should be used for projects of similar category. It will be the
responsibility of project teams to establish how the poor affected by a project
can be identified, and where an income/expenditure measure of poverty is
not to be used, this omission should be justified.
The remaining part of the Handbook should be directly relevant for a
wide range of projects at present financed by ADB. These are projects that
are well-defined in the sense of having identifiable and quantifiable outputs
produced by tangible inputs. Even projects for which outputs are quantifiable
(for example, numbers of pupils, patients, houses, etc.) but cannot be readily
valued in monetary terms can be incorporated into the framework set out
below. However, projects outside of this category, where outputs are defined
more broadly, such as institutional development in a policy reform context,
would need a different approach under a separate study. Projects to promote
small enterprise development, while potentially highly significant in terms of
poverty reduction, also have benefits that are too diffused to make a meaningful
ex-ante assessment. Financial analysis of such projects normally proceeds
on the basis of calculations on representative projects, whose returns may be
aggregated to form an economic return. However, this practice is too
approximate for meaningful distribution analysis. Here it is probably far more
effective to concentrate on setting clear priorities for financial intermediaries
and ensuring that they operate within a well regulated financial environment.
For ex-post evaluation, however, there can be a range of evaluation techniques
for this category of projects (e.g., Khandker 1999).
Resource implications are important in operationalizing poverty impact
analysis. The World Bank handbook (Baker 2000) for ex-post poverty impact
evaluation studies indicates that the share of such studies in the total project
cost can vary widely from 0.2 to 1.3 percent. It is likely that the analysis relevant
for ex-ante project appraisal will not be able to match the time and resources
required for such ex-post studies. Appendix 8 provides a preliminary indication
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
of resource requirements under project preparatory technical assistance. The
next few years could be considered as a pilot test period for this Handbook.
For distribution and poverty impact analyses to become meaningful, it is
important that ESW is strengthened to cover poverty issues associated with
each country and major sectors where project identifications are proposed.
It is recommended that future ESW carry out a diagnostic analysis on issues
that are discussed in Chapter 2 to provide poverty-focused contexts of project
preparation.
Pre-PPTA Stage
If poverty focus is to be built successfully into project work it is obvious
that it needs to be considered as early as possible in the project planning
cycle. At the fact-finding stage of the project preparatory technical assistance
(PPTA) the question must be raised as to whether or not a project should be
considered as a Poverty Intervention. Technical nature of the project type
may well constrain the extent of targetability. At this stage it is recommended
Box 1
• Explain definition of poverty used (e.g., poverty line, or some basic needs indicator).
• Identify and estimate the coverage of the poor groups affected to the extent meaningful.
• Analyze causes of poverty in project area of influence to the extent meaningful.
• Explain the mechanism through which the poor are affected (e.g., as consumers
through lower prices, as nonpaying users, as workers through new jobs, as producers
using services of the project as inputs).
• Explain the critical assumptions required to achieve poverty impact (e.g., policies for
targeting, uptake by the poor, willingness to pay by the poor, financial sustainability
of project).
• Explain the risks of failure in achieving poverty objectives.
• Examine scope for leakage of benefits to nonpoor.
• Consider possibility of project encountering financial difficulties.
• Discuss preliminary measures available to reduce the risks.
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Approach to Poverty Impact Analysis for Poverty Intervention Projects
13
that the questions described in Box 1 be addressed in the initial social
assessment (ISA). It is recommended that the project's envisaged impact, the
mechanism through which it will improve the position of the poor and the
assumptions required for this to be achieved, be presented. Considering the
typical time constraint for PPTA fact-finding missions, it is recommended that
as much existing poverty data relevant to the envisaged project as possible
be collected prior to the mission (Appendix 3).
PPTA Stage
At this stage, projects that are to be Poverty Interventions will require
detailed socioeconomic assessments and detailed questions on poverty impact.
This will require a more precise indication of the poverty impact of the project
in terms of numbers of the poor affected and, wherever possible, estimates
of their net benefits (expressed in money-metric terms) due to the project.
The assessment should provide the basic data that can be used to extend
project economic analysis to incorporate poverty impact analysis. There must
be a discussion of ways of reaching the poor and ensuring that leakage of
benefits to nonpoor is minimized (minimization of "type I error" relative to
perfect targeting). Refined leakage minimization measures should be addressed
at this stage. In the final project design, it should be ensured that credible
instruments exist for targeting and monitoring poverty impacts.
A poverty focus requires that poverty concerns are fully incorporated
at the project design stage. Where possible, alternative ways of implementing
a project, for example in choice of technology, in location or in type of service,
should be considered with a view to maximizing net benefits to the poor,
subject to the overall constraint that where net benefits can be monetized
the design selected must achieve a minimum acceptable EIRR of 12 percent.
(Maintaining the current economic decision criteria is unquestionable in light
of ADB's opportunity cost of development fund.) At the stage when project
design and location are being decided, poverty issues should be considered
wherever possible. The economic analysis appendix of the report and
recommendation of the President (RRP) should discuss project (design)
alternatives that are considered. While discussion of the alternatives has been
the standard requirement for all projects, poverty reduction should now be
the added dimension for consideration. Box 2 illustrates the type of questions
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Box 2
• Explain the alternatives considered (e.g., location, type of service, technology, scale).
• Explain the poverty impact of the various alternatives; this can be quantitative (e.g.,
number of jobs) or qualitative where firm data are lacking (e.g., access to improved
facilities).
• Justify the alternative selected on both economic efficiency (e.g., higher economic
NPV) and poverty reduction grounds (e.g., cost per unit of net benefits received by
poor.
to be addressed. The discussion can be brief provided a clear case can be
made to justify the final design and packaging of the project.
Poverty impact analysis should be applied to the extent the analyst
considers practicable in the economic analysis appendix of the RRP for all
Poverty Interventions. It could also be applied to Other Interventions wherever
data are available and the analyst finds it a useful tool in considering alternative
projects or project designs. It should aim to calculate a poverty impact ratio
(PIR), that is, the share of the poor in net benefits of the project (note, however,
that the PIR in itself is not the target for maximization as cautioned in Chapter
5). Where, as for example in primary health or sanitation projects, benefits
cannot be valued realistically in monetary terms, the second-best will be a
headcount approach, that is, to estimate the poor as a proportion of total
beneficiaries.
Steps for Poverty Impact Analysis
The extension of standard economic analysis of a project to poverty
impact analysis requires first a distribution of project net benefits—setting
out the effects of a project for the groups that gain or lose—and second, a
poverty impact analysis in which the proportion of each group's gains or losses
that goes to the poor is estimated. Hence a poverty impact analysis cannot
be carried out without first undertaking a distribution analysis on a project
(as can be seen from Step 5 in Box 3). Gains and losses arise first from the
financial arrangement of a project. In addition they can arise from the distortions
or externalities that are captured in an economic analysis. The economic NPV
of a project, which measures its full contribution to national welfare, will be
the sum of its financial effects and its external economic (economic minus
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3
Approach to Poverty Impact Analysis for Poverty Intervention Projects
15
Box 3
. Set out the annual financial data on the project showing inflows (revenue
and loan receipts) and outflows (investment, operating costs, loan interest and
principal repayments, and tax both on profits and purchased inputs) from the
perspective of the project owners. This is sometimes termed a return to equity
calculation at constant prices. As this aspect of financial analysis is in fact a departure
from current normal ADB practice, Appendix 4 illustrates what is involved in
distribution analysis with reference to actual ADB projects. Appendix 5 illustrates
what is required in the project financial analysis to obtain the data needed in
distribution analysis. See also Appendix 23 of the ADB Guidelines (1997) for
illustration of financial returns to equity. This part of distribution analysis can only
be done fully after the project financing plan is finalized (since only then will the
loan-equity split be known), but in practice its omission may not make a great
deal of difference to poverty impact estimates.
Discount each annual inflow and outflow to derive present values for each
category and a net present value (NPV). Normally a 12 percent discount rate should
be used for these calculations. The resulting NPV will be a financial NPV showing the
income change for project owners. In addition there will be a gain to government from
tax payments and where subsidized loans are provided, a loss to lenders.
Note: The choice of the 12 percent discount rate is for the purpose of analytical consistency.
As the methodology applied in the Handbook is ultimately concerned with economic
analysis, the 12 percent is the natural choice. However, obviously the financial opportunity
cost of capital faced by the project entity (or various stakeholders) need not coincide with
12 percent. This issue is discussed in Fujimura and Weiss (2000). Financial viability test
of the project should follow the usual practice (with the weighted average cost of
capital, WACC, being the proxy for financial opportunity cost of capital). See ADB
(2001a) for strictly financial aspects of project analysis.
Identify the economic value to be used for each project input/output category.
The ratio between this economic value and the financial price for actual transactions is
the conversion factor (CF) for the item concerned. Normally for distribution analysis it
is simpler to conduct economic appraisals in the domestic price numeraire (which means
that income from the financial and economic calculations will then be in the same price
units). If a world price numeraire is required for the economic calculations, to carry out
a consistent distribution analysis, all financial data from steps 1 and 2 must be converted
financial) effects. The ADB Guidelines (Appendix 1) explain how the analysis
works. The conceptual issues associated with these procedures are
discussed in Fujimura and Weiss (2000). Rather than repeat these discussions
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
to world prices by multiplication by the standard conversion factor (SCF) (see Case 1 in
Appendix 6). Nonetheless, it is recommended that the domestic price numeraire
be used consistently.
Express all project items in economic terms. This can be done by applying CFs
to revalue the financial data from step 1. If CFs are taken as constant over the project's
life, only the present value figures at step 2 need to be adjusted. For items for which there
is no financial value at step 1 (for example, an environmental cost for which a project
itself is not charged), their economic value, wherever estimated, should be entered
directly in the economic benefit flows.
In practice, where project analyst did not foresee the need for distribution analysis and
has done the conventional economic analysis first, as would be the case for most project
preparations prior to the wider application of distribution analysis, the analyst could work
backward to arrive at financial benefit and cost streams using conversion factors and
transfer payments (see Case 2 in Appendix 6).
Allocate any difference between financial and economic values to particular
groups. These plus the changes for project owners and others at step 2 give the net
benefits created by the project. The net benefits to different groups must sum to the
economic NPV of the project, since this measures the total net benefits of the project.
This can be seen as an identity: economic NPV = financial NPV + (economic NPV financial NPV).
Note that where there is no financial revenue for the project agency, as in the case of
road rehabilitation projects, net benefits still need to be distributed between different
stakeholders (see Case 5 in Appendix 6).
! A poverty focus requires that for each identifiable group affected by a project, the
proportion of those who can be classified as poor be estimated.
" Estimate for each group affected by a project the proportion of net
benefits that will go to those below the poverty line. Groups involved will vary
between projects but will typically include consumers, workers, producers,
government and the rest of the economy. For the government what is required
is an estimate of the counterfactual; i.e., what proportion of government expenditure
diverted from other uses by the project under consideration would have otherwise
benefited the poor. Similarly if a project generates government income a proportion
of this will create benefits for the poor, which will be indirectly caused by the
project concerned (see Chapter 4).
# Finally, sum all net benefits going to the poor and divide by the total
net benefits (economic NPV). This result is termed the poverty impact ratio (PIR).
here the basic steps required to carry out these analyses are set out in summary
form (Box 3). Appendix 4 illustrates actual distribution analyses of ADB projects.
Appendix 5 shows how a financial analysis desirable from a distribution
perspective can be structured. Appendix 6 illustrates poverty impact
calculations, based on materials from recent ADB project appraisals.
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Introduction
17
4
Data Requirements
though the methodology for distribution and, by extension, poverty
impact analysis has been around for more than two decades (see
Fujimura and Weiss 2000), there remains a fair amount of data problems
in application. Of the parameters to be estimated in Step 6 of the analysis (Box
3), two are the most fundamental. One is benefit incidence—the extent to
which it is possible to distinguish between the poor and nonpoor among direct
project beneficiaries. The other is the problem of establishing the indirect
impact on the poor of the gains/losses that accrue to the government and the
rest of the economy.
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Benefit Incidence
The estimation of how the poor among direct beneficiaries are affected
by a project can be complex. For certain types of projects with a direct poverty
focus, the poor should be the sole direct beneficiaries. This is most obvious
where there is geographic targeting, so a school or health clinic is located
in areas in which only the poor reside. Similarly, there may be effective selftargeting, where only the poor take up the services of the project; for example
a local clinic, where the better-off would use a hospital, or a public works
scheme, where only the poor would seek employment at the wage offered.
Means of effectively targeting the poor would have to be part of the initial
project design.
On the other hand, there may be less clearcut cases. Provision of water
or electricity to an area may involve the connection of households, some of
which are above and some below the poverty line. The project socioeconomic
assessment or similar field data collection during PPTA would have to identify
the approximate distribution of users and how the project will affect these
different groups. Box 4 gives an indicative terms of reference for such
socioeconomic assessment from the poverty impact perspective.
It may be that for some projects regional or district level estimates of
the headcount index of poverty may be available (e.g., poverty mapping
discussed in Appendix 3; Bigman and Fofack 2001), which can be used to
infer the degree of poverty in the population served by the project. In the
Box 4
The assessment should
• identify the potential beneficiary (and/or loser) groups, separating those below the
poverty line; in doing this, collect and use as much existing survey data as possible
(Appendix 3);
• describe the current status of the target population (either in income/expenditure or
more general socioeconomic characteristics);
• examine the need or demand for the project by the target population and where
appropriate assess how much they are willing to pay or forego to receive its services;
and
• generate information to allow community participation in the design of project activities
and in its implementation.
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4
Data Requirements
19
absence of such data, however, alternatives will have to be found. A special
household survey, as part of ADB's PPTA, is an option. However, given the
difficulties in quantifying household income (e.g., where there is significant
subsistence activity and multiple family jobs) or expenditure (e.g., recall bias)
in one-time surveys, it may be appropriate and more cost-effective to use
such a survey to establish specific household characteristics (such as lack of
clean water, adequate shelter or access to primary education), which can be
used to identify the existence of poverty. For example, a proportion of the
families without access to clean water in an area can become the approximate
headcount poverty measure. Such proxy indicators were found to be useful,
for example, in the World Bank study on Poverty in Cambodia (World Bank
1999). Appendix 3 discusses how existing survey data can be utilized to assist
poverty impact analysis prior to and during the PPTA.
An important part of any project plan will be to ensure that not only
are target beneficiaries identified but also that there is a delivery mechanism
that ensures that benefits accrue primarily to the target group. However,
whether or not the delivery mechanism includes user charges can have a
significant impact on the uptake of project services. Here consideration needs
to be given to the balance between the project's financial sustainability and
the poverty impact on the direct beneficiaries, since even if price elasticity
of demand may be low it will not be zero (see ADB August 2000, for a summary
of different approaches to demand elasticity estimation), while of course user
charges can be pro-poor from a broad fiscal perspective. Another aspect of
a socioeconomic assessment must establish how much the poor currently
pays for use of services, where payment may be either direct in fees or charges
to existing providers or indirect, for example in terms of travel costs or the
opportunity cost of waiting time. In addition, it will be important to estimate
the demand for new services at likely levels of charge. In many instances,
estimates of actual payments and potential willingness to pay may need to
be only approximate. However, in some cases (e.g., water and sanitation),
willingness to pay for new connections may be an essential variable in both
tariff setting and benefit valuation. Detailed estimates of willingness to pay
are best left to specialist studies such as application of contingent valuation
method (CVM) that would be additional to and complementary with socioeconomic assessments. [ADB (March 1996) provides appendixes on CVM
application for environmental valuation. ADB (March 1999) provides an
appendix on CVM in water supply projects. Choe et al. (1996a and 1996b)
provides detailed applications of CVM. See also Anand and Perman (1999),
Carson (2000), Singh et al. (1993) and Whittington et al. (1993).]
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Where projects have effects that are spread widely, targeting may
involve considerably more than simply location. For example, an irrigation
project may in principle benefit farmers from a range of income strata. Ensuring
that a significant number of beneficiaries are poor may require provision of
a package of support services to poor farmers to allow them to gain the
maximum crop increase from the availability of irrigation water. Seeds and
fertilizer and the credit to purchase these will be required, as well as the
information on their application. A road project would have inherent difficulty
in associating its beneficiaries with geographical areas the road passes through.
In such case, a special study such as origin/destination survey may be required
in addition to the usual traffic projection. A socioeconomic assessment need
not set out the exact mechanisms involved but it should draw attention to the
issues.
Government Net Benefit and Effects on the Rest of the Economy
Since governments will be major participants in ADB projects even
where they have no direct equity commitment, a critical parameter in poverty
impact analysis is the extent to which government expenditure and taxation
accrue to the poor. In other words, to apply the approach rigorously we require
an estimate of the benefit/tax incidence for the poor from marginal government
expenditure and finance. This is particularly important where poverty-focused
projects receive subsidies and therefore draw income from the government
budget. However, this parameter is not available for any country and its
derivation on a country-specific basis would need to be the subject of separate
and extensive research. To avoid complications, marginal government
expenditure/tax can be treated as distribution-neutral: additional unit of
government net benefit induced by the project will be distributed back to the
economy in a neutral way. Such distribution-neutral incidence of government
net benefit on the poor will conceptually be represented by the weighted
average of the expenditur
e and tax incidence on the poor, which will need
expenditure
to be estimated in country-specific studies. The analyst might make an upward
or downward adjustment from the weighted average if she/he is convinced
of a specific way in which project-induced income/financing will be distributed
(somehow pinning down the fungible public fund), provided that it can be
substantiated by convincing empirical evidence.
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4
Data Requirements
21
However, the weighted average incidence parameter is unlikely to be
available in the current state of knowledge in, for example, public expenditure
and finance reviews. Until such knowledge becomes available, further
simplifying assumption (admittedly with no rigorous theoretical underpinning)
can be applied: if the government spends additional funds created by a project
or forgoes spending as funds are displaced by a project, these effects will not
alter the distribution of income and the poor will share in the government net
benefit in direct proportion to their share in the gross domestic product (GDP.)
This simplification amounts to regarding the incidence of government net
income as being equivalent to the effects of the rest of the economy on the
poor (that can be referred to as economy effects). This is in line with the
convention of project appraisal in which the shadow exchange rate factor
(SERF) premium is treated as equivalent of any other form of government
income. The assumption used is that the current state of income distribution
is considered as the natural outcome of how an exogenously available extra
unit of resources will be distributed in the economy. While this parameter is
rarely known or documented, Appendix 7 shows how an approximate estimate
may be derived from country-specific survey data and gives a list of preliminary
calculations for DMCs where necessary data can be obtained. In most cases
the income share of those below the poverty line will be low, and well below
their headcount share in population. (For example, the estimate for Viet Nam
is 12.6 percent while the headcount poverty incidence is 37.4 percent.) These
can be used as the proxy for the unknown parameter for the economy effects,
and if no alternatives are available, also for the incidence of government net
income on the poor. Note, however, that this figure will only be available in
financial terms while project net benefits will have to be in economic terms.
Where country-specific poverty data/poverty lines are not available, it is
cent could be used as a rrule
ule of thumb
proposed that 10 per
percent
thumb. This is roughly
the upper bound for the share of the lowest quintile in income distribution for
DMCs (e.g., World Bank 2000f, p. 282, Table 5).
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Box 5
! "
The assumption made about how the government net benefit would otherwise have
been distributed and hence the degree to which it would have accrued to the poor can
be significant in determining a project's poverty impact ratio (PIR). Where projects are
net users of government income rather than generators of additional income for the
government, the lower this parameter the higher will be a project's PIR. This can be
illustrated with a simple numerical illustration. We assume a project ENPV is distributed
as follows.
Farmers
Consumers
Government
200
50
(100)
ENPV
Gain to Poor
PIR
150
$ $ $ $ 0.8
0.1
0.5
0.8
0.1
0.1
160
5
(50)
160
5
(10)
115
0.77
155
1.03
In case 2 with the lower proportion of the poor in government net benefits (0.1 rather
than 0.5) the PIR rises to above 1.0. On the other hand, projects which are net income
generators for the government, for example, through a commercial pricing policy by
public utility projects, will have a lower PIR, the lower the value assumed for the government
net benefit parameter.
Where government income is only marginally affected by a project (e.g., due to cost
recovery pricing and low distortions in the economy) the overall PIR will not be sensitive
to this parameter. Nonetheless, when the assumption about the government income
parameter is uncertain, a sensitivity analysis for PIR with respect to this parameter
is recommended (see Case 5 in Appendix 6).
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Introduction
23
5
Advantages
and Limitations
of the Analysis
Improving Project Quality at Entry
There are several advantages in carrying out distribution and poverty
impact analysis of projects that are relevant regardless of project classification.
First, the analysis forces a more thorough cost-benefit analysis. For example,
in the analysis of agricultural projects, focusing on distribution and poverty
impact forces separate consideration of different farm sizes when preparing
farm-income statements, which in turn may lead to a more detailed analysis
of the rate of adoption of technology, contributing to better accounting for
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differences in expected productivity increases among farmers (Londero 1995).
Second, the estimation of net benefits accruing to different stakeholders will
help clarify who are the major beneficiaries and losers of the project and
sharpen the judgement of project sustainability from the financial and political
economy perspective. The analysis can assess the consequences of different
financial and institutional arrangements on the incidence of net economic
benefits on the poor. By establishing the link between financial and economic
analysis and rigorously eliciting the transfer payments, especially direct and
indirect subsidies entailed, such analysis will highlight financial sustainability
issue in a much stronger manner (Case 2 in Appendix 6). Third, the analysis
brings up policy questions in an illuminating way. For example, a
noncompetitive market structure may prevent adequate benefits from reaching
the poor (Case 5 in Appendix 6).
Complementing the Inadequacy of Headcount Approach
The World Bank and Inter-American Development Bank (IDB) use project
classification systems similar to ADB’s, in which a category of targeted projects
is given unique attention under a poverty reduction objective. Criteria for
this category of interventions rely crucially on the beneficiary headcount
approach. The World Bank’s criteria rely either on beneficiary headcount or
a specific mechanism of targeting the poor (World Bank 2001), while IDB’s
criteria are based on the combination of three criteria: sectoral, geographical
and headcount (IDB 2001). In the case of ADB, the classification (November
2000, page 3) states, “The criteria used for poverty classification for loans are
(i) the proportion of poor among the beneficiaries, and (ii) impact or benefit
analysis.” This foresees that both headcount and money-metric measure of
net benefit incidence are applicable. This is based on the recognition that a
headcount beneficiary measure is a blunt and often misleading measure of
poverty impact. It fails to capture the depth of poverty impact and can either
overstate or understate the money-metric measure. For example, overstatement
occurs when project net benefits are spread thinly over a large number of
threshold-poor, while understatement occurs when project net benefits are
concentrated on a small number of hard-core poor. Therefore, one would
prefer to apply the money-metric measure of poverty impact wherever
technically feasible and practicable under available data and resources.
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5
Advantages and Limitations of the Analysis
25
There are some obvious inadequacies with the headcount approach:
beneficiaries must include those affected negatively as well as those who
are targeted for service delivery. In many projects that are net users of public
funds, the population outside the projects’ influence areas are the losers. Should
the number of such losers be deducted from the number of gainers according
to poverty level? This will not likely be done in practice. This vagueness remains
in defining what is meant by beneficiaries itself when looking at the cost side
of the project (Londero 1999). Furthermore, the headcount approach tells
ty impact
nothing about efficiency of delivering pover
poverty
impact. While some targeted
projects may provide tangible benefits to the identified beneficiaries, the
question remains whether their delivery mechanism is cost-effective. A full
poverty impact analysis set out in this Handbook can check the efficiency of
poverty reduction in this regard. By using the information obtained in the
poverty impact analysis, the indicator for efficiency of poverty impact can be
calculated as the net benefits going to the poor per unit project cost. This can
be a ranking index among, for example, alternative subprojects (this point is
illuminated in the discussion in Case 2 of Appendix 6).
With care and qualifications (page 27), poverty impact analysis can be
used to provide economic underpinning of Poverty Interventions. Provided
that PIR estimation has been judged practical and carried out, the requirement
of a project disproportionately benefiting the poor translates into an EIRR of
12 percent or above plus a PIR significantly greater than the poor’s share
in GDP
GDP.. Here the PIR works as a money-metric counterpart of headcount
poverty incidence among the project beneficiaries, while the poor’s share in
GDP works as a money-metric counterpart of national headcount poverty
incidence. Appendix 7 provides a list of approximations for the poor’s income
share in GDP for selected DMCs. Depending on the agreed poverty line to be
used in the analysis, the figure for the poor’s share in GDP may be excessively
low or high. Where the figure for this parameter is unavailable or unreliable
cent could be used as a rrule
ule of thumb
for one reason or another, 10 per
percent
thumb. As
in the headcount approach, how to interpret “significantly higher” is open
to discussion and review.
For many Poverty Intervention projects it may not be possible to monetize
benefits and hence an EIRR is not calculated. For example, in many social
sector projects, benefits will be identified in quantity form (e.g., patients, years
of life, pupils, years of schooling, number of dwellings). Where benefits are
not in money-metric terms, the twin requirements of efficiency and poverty
impact must still be applied, but in a different manner from that described
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above. Here efficiency requires that the project be cost-effective in providing
the service concerned, e.g., in cost per patient, cost per year of life saved,
or cost per slum dwelling cleared. Cost-effectiveness should in principle be
calculated by taking the ratio of the discounted stream of project costs over
its operating life to the discounted stream of physical output. Hence the unit
cost of service provision must be derived in discounted terms as an average
incremental economic cost (AIEC). The comparator for testing whether a cost
is excessive will have to be built up from experience of similar project
interventions in the country and elsewhere, as is recommended for health
sector projects (ADB August 2000). For such projects where project benefits
are not monetized, full PIR analysis is impossible and poverty impact estimation
would be limited to a headcount basis (Case 4 in Appendix 6).
Variety of Projects
One obvious limitation of the distribution and poverty impact analysis
is that it cannot be expected to cover all types of projects and comparability
across projects cannot be any better than that of conventional project economic
analysis as it is practiced. This limitation is nothing new and is a carryover
from the limitation in the current practice. At one end of the range are projects
in sectors such as power, water and irrigation, where full benefit-cost analyses
are regularly applied and where the use of distribution and poverty analysis
may be a natural extension of current work. At the other end of the range are
projects in social sectors such as health and sanitation and primary education,
for which EIRRs are rarely calculated for practical purposes. Such projects
can be subject to cost-effectiveness analysis and increasingly a form of such
analysis is being applied. As explained above, alternative criteria can be applied
for poverty-focused projects where monetary estimation of benefits is not
possible and beneficiaries may be, for example, in terms of number of poor
patients or poor pupils. Between these extremes will be a range of intermediate
situations, such as in roads, where there may be technical difficulties relating
to distribution and poverty analysis. However, it is encouraging that ADB’s
recent transport projects are testing the methodology (see Appendix 8).
Projects for which the methodology is very difficult to apply will be, for example,
in relation to institution building and private sector development, since it will
be difficult to relate investment expenditures to tangible outputs and income
flows.
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Advantages and Limitations of the Analysis
27
Caution on Interpretation of PIR Index
Early experience at ADB has provoked two concerns. First, some may
hastily interpret that the PIR index is a summary indicator for poverty impact
as the EIRR is a summary indicator for project economic viability. This is not
the case. The PIR by itself is merely the proportion of the NPV accruing to the
poor against the total project NPV and does not inform poverty impact ranking
or efficiency of poverty reduction among alternative projects (designs). It is
conceivable that in some cases, a deliberate attempt to raise the PIR would
reduce poverty impact in absolute terms and defeat the purpose. What projects
should be maximizing is not the PIR index but the NPV going to the poor
(absolute poverty impact), or the NPV going to the poor per project cost
(efficiency of poverty impact).
Second, while in principle the PIR is a superior index to the headcount
poverty index in interpreting the meaning of “disproportionately benefiting
the poor,” it is often an uncertain point estimate. As explained in Chapter 4
(Box 5), the PIR is usually sensitive to crucial parameters, assumptions for
which are uncertain. Therefore, the analyst needs to use a sensible judgement
on whether the full poverty impact analysis makes sense and avoid mechanical
application of the PIR. When it is calculated, sensitivity tests are strongly
recommended with respect to uncertain parameters such as the poor
proportion of net benefits accruing to the government. Sensitivity test on the
PIR can be useful in illuminating policy discussion if it is done on policy
parameters such as utility pricing (Appendix 1) and market competitiveness
(Case 5 in Appendix 6).
Risk for the Poor
Poverty is normally associated with vulnerability, so that unexpected
unfavorable outcomes may have very serious consequences for those at or
below the poverty line. ADB project appraisals in the past have rarely addressed
issues of risk of project failure with a few exceptions in the energy sector.
ADB Guidelines (1997, Appendix 21) have only a brief discussion on risk
analysis. The possibility of pooling risk across projects provides a rationale
for omitting risk calculations from the decision criteria, but it does not apply
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when one is considering particular groups. Fujimura and Weiss (2000)
illustrate how in principle a risk of failure, defined as the probability that a
project makes the poor worse off, can be estimated. The idea is to replace
point estimates of project net benefits accruing to particular groups of
beneficiaries by their probability distribution. This subject is taken up in a
subsequent EDRC study (ADB 2001).
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CHAPTER 1 Introduction
29
6
Linkage to PolicyBased Lending
t is worth noting that project ideas emerge from sector-level work and
the proposed project should address some type of constraint identified
in a sector-level analysis, which cannot be addressed if left to private
sector activities. Logical framework requires that projects be identified to
improve sector economic performance for the benefit of the entire economy.
When in some cases project identification is based on inadequate sectorlevel analysis or project formulation is based on unclear economic justification,
the process of project economic analysis can proactively raise policy issues.
For example, the extent of difference between financial and economic prices
(and therefore the consequences for estimates of FIRR and EIRR) usually
implies something fundamental about the nature of the policy environment
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within which the project is being prepared (ADB June 1999). If such difference
can be traced to either sector-level and economy-wide policy or institutional
anomalies (that hinder efficient workings of the economy), there may be
room to address the former within the project design (e.g., removal of state
monopoly in a particular project input or output market) before the full
investments take place. Or alternatively, in consultation with the counterpart
government, consideration can be given to resorting to sector development
program modality with a policy loan component. The project analyst should
examine whether the project’s envisaged benefits could be jeopardized by
the current policy and institutional environment and whether this should
be addressed through policy- based interventions including high-level policy
dialogue. The early findings from a separate EDRC study (Bolt and Fujimura
2001) point to the importance of political economy process in assessing links
between policy-based lending and poverty reduction.
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1
Appendixes
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Introduction
31
32
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1
APPENDIX
Introduction
33
1
(reproduced from Appendixes 25 and 26 of the ADB
Guidelines for the Economic Analysis of Projects)
APPENDIX 25: DISTRIBUTION OF PROJECT EFFECTS
The costs and benefits of a project are shared among different groups.
There are several ways in which the distribution of project effects can be
analyzed. First, the project effects can be allocated among different project
participants, usually suppliers, consumers, owners, lenders, workers or
producers, and the government representing the rest of the economy. It is
usual to expect owners, lenders, workers or producers, and the government
all to share in the net project effects. Frequently, consumers and suppliers
also do. Second, for projects that involve foreign investors, lenders,
management, and labor, the distribution of net project effects between nationals
and foreigners can be demonstrated. Third, project effects can be allocated
between the public and the private sectors. This may be particularly important
for infrastructure developments where public sector expenditures are made
in support of private sector operations. Fourth, the net project effects can be
allocated not only among different project participants but also among
participants with different income levels. Fifth, net project effects can be
allocated according to whether the project net benefits are likely to be
consumed or saved. Finally, costs and benefits can be allocated among different
countries participating in subregional projects.
Considerable effort was expended in the past to include the distribution
of net benefits between savings and investment into project analysis. The
purpose was to identify and give priority to projects that would enhance
savings, and therefore investment in the economy, by applying a premium to
project effects resulting in extra savings. Considerable effort was also expended
to include the distribution of net benefits by income group into project analysis.
The purpose here was to identify and give priority to projects that would
enhance incomes for lower income groups, by applying a different weight to
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34
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
the incremental incomes of different groups. However, both forms of analysis
depend on specifying premia that are essentially subjective and open to
disagreement. In addition, enhancing savings can lead to priorities that
contradict the enhancement of incomes for lower income groups, and both
savings and the distribution of income can be affected more directly by policy
changes at the national level rather than through the net effects of new
investment projects.
It is recommended that simply a statement of the distribution of project
effects be given, without applying any premium to either incomes that are
saved or to incomes accruing to particular income groups. There are three
reasons for providing such a statement. The first is to assess whether the
likely distribution of project effects corresponds with the objectives of the
project. The second is to bring the financial and economic analyses of projects
together to ensure that the consequences for the economic benefits of projects
of changes in financial arrangements are assessed. The third is to assess the
likely impact of policy changes on the distribution of project effects.
The following example illustrates the construction of a statement on
the distribution of project effects. For simplification, the project excludes the
effects of project financing, that is, it does not consider the possible net costs
or benefits to lenders. Neither does it include direct tax payments. The
illustration concerns a telecommunications project involving 50,000 new lines
and associated exchanges that will extend the national network into a rural
area through the provision of publicly accessible telephones in villages and
rural towns. The analysis of the distribution of project effects is based on the
incremental number of calls for the telecommunications corporation and the
incremental costs of providing the new telephone facilities. Values for the
costs and benefits of the project, at both financial and economic prices, are
all given as present values calculated at a discount rate of 12 percent
representing the economic price of investment funds in the economy.
The forecast project financial statement at constant domestic market
prices is summarized in Table A1.1. At the projected future charge level, which
will apply across the whole telecommunications network and not just in the
project area, the telecommunications corporation will not recover the full
incremental costs of the project at financial prices inclusive of the opportunity
cost of capital. The corporation will have a loss on resources in present value
of 100.
The economic analysis of the project introduces three major
considerations. First, with the project telephone calls will be made at a cost
that includes the telephone charge going to the corporation plus the costs of
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APPENDIX 1 Distribution and Poverty Impact Analysis
35
Table A1.1
! # %
& ' (
Benefits
Revenue
700
Costs
Equipment
Installation
Operating Labor
Other Operating Costs
400
100
100
200
) $
, %
*++
&++(
traveling to the telephone. Without the project a high proportion of telephone
users would continue to communicate through other means, including
traveling to the call destinations. The difference between the cost of
communication without the project and the full costs with the project, including
the costs of reaching the telephone, represents an economic benefit to telephone
users that is not incorporated in the financial charge for the telephone calls.
In addition, a further economic benefit will stem from the fact that several
small businesses and farmers will benefit from the better access to
communication relating to input and output markets and prices, and transport
schedules. Taken together these additional economic benefits can be added
on to the financial revenues as a consumer surplus. Second, there is a difference
between the economic price of foreign exchange and the official exchange
rate. A shadow exchange rate factor (SERF) of 1.3 has been estimated for the
country, implying that foreign exchange costs have a higher economic than
financial cost to the economy. Third, there is a surplus of labor that could
easily be trained for telecommunication operations in the area. The opportunity
cost at domestic prices for operating labor has been estimated as 90 percent
of the wage level, in other words a shadow wage rate factor of 0.9.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
The financial project statement has been adjusted by the consumer
surplus and the appropriate conversion factors to derive the project economic
statement in Table A1.2. The economic values have been expressed at the
domestic price level in national currency. Table A1.2 also shows the differences
between the financial and economic value of resources. These differences
give rise to losses and gains among the project participants. As indicated by
the consumer surplus, consumers of the new telephone services benefit to
the extent to which the economic value of communication cost savings and
business efficiency improvements exceed the full cost of making calls. The
economic valuation of the equipment for the project exceeds its financial value
to the extent of the SERF; the consequent loss because of the overvaluation
of the exchange rate is borne by the government representing others in the
economy, especially importers. The financial cost of labor exceeds its
opportunity cost; the difference accrues as a gain to operating labor. These
gains and losses are complemented by the loss to the corporation because
not all the full financial costs, including capital costs, are recovered. The righthand-side section of Table A1.2 summarizes these gains and losses to different
project participants.
The overall results for the project are a financial net present value (NPV)
of minus 100 and an economic NPV of 40. The economic NPV exceeds the
financial NPV by 140. More specifically, as at present structured, two
participants lose from the project. The corporation will suffer a loss of 100,
and the rest of the economy will suffer a loss of 120 because foreign exchange
is available at a price lower than its economic price. On the other hand, two
participants will gain. Operating labor will gain by 10 at the project wage
level, while consumers will gain from their consumer surplus of 250. These
gains and losses in part compensate for each other; the net gain is positive
and is equal to the economic NPV of 40.
The differences between the financial and economic values, and the
consequent gains and losses for different project participants, provide the
basis for considering the impact of policy changes. First, there is a small gain
to labor. If there were a completely competitive market for labor this would
not occur. However, this is not a major source of difference between financial
and economic outcomes. Second, a substantial gain has been identified
accruing through the corporation to the consumers as a result of revaluing
the foreign exchange element of project inputs. This gain could only be corrected
by a general realignment of domestic and world prices, outside the context
of project level changes.
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APPENDIX 1 Distribution and Poverty Impact Analysis
37
Table A1.2
" !
$ %&' (
Difference
Distribution of Project Effects
Benefits
Revenue
Consumer Surplus
700
1.00
1.00
700
250
0
250
! (400)
(100)
(100)
0.30
1.00
0.90
(520)
(100)
(90)
(120)
0
10
(200)
1.00
(200)
0
910
40
110
140
Costs
Equipment
Installation
Operating Labor
Other Operating
Costs
"
& 800
(100)
250
(120)
10
#$%
(100)
(120)
10
250
Finally, the main beneficiary of the project will be the consumers. In
part, their benefits could be incorporated into the telephone charges they
must pay. If the telephone charges were raised to cover the financial loss of
100, that is, by 100/700 or 14.3 percent on a project basis, most consumers
would still be making substantial gains. However, the most marginal consumers
may not use the new telephone in these circumstances and so some of the
economic benefits would be lost. Moreover, any change in telephone charges
in real terms would also impact on existing network users.
The distribution of project effects is of interest for its own sake. It also
assists those designing projects by drawing attention to the effects of current
policies on the financial and economic results, in this case, the exchange rate
and pricing policy. Changes in project design within the current policies can
be assessed within the same framework. The effects of policy alternatives
can be presented in this way to inform policy dialogue with governments
and the stipulation of loan covenants.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
APPENDIX 26: IMPACT ON POVERTY REDUCTION
Poverty reduction is the most formidable development challenge. To
reduce poverty some projects target the poor directly, but most aim at economic
growth, benefiting the poor indirectly as well as directly. This appendix shows
how to trace the economic impact of growth projects on the poor.
The poverty-reducing impact of a project is traced by evaluating the
expected distribution of net economic benefits to different groups. With financial
prices determining who controls net economic benefits, the first step is to
estimate the present value of net financial benefits by participating group.
Next, the difference between net benefits by group at economic and financial
prices is added to net financial benefits by group to give the distribution of
net economic benefits by group. Finally, the net economic benefits accrue to
the poor according to the proportion of each group that is poor. A poverty
impact ratio expressing the proportion of net economic benefits accruing to
the poor can be calculated by comparing net economic benefits to the poor
with net economic benefits to the project as a whole.
This can be illustrated through a publicly funded water utility project
selling piped water. The water supply project serves a small rural town. All
capital equipment is imported, subject to an import tariff. Labor and electricity
account for total operating and maintenance (O&M) costs. Wages are controlled
by a minimum wage law, with the economic price of labor being a proportion
of the minimum wage. Electricity is subject to a sales tax and a production
tax. The water utility is not subject to income tax. All financial and economic
values are given in constant year-of-appraisal prices and in present value
terms. Tradables are valued at border prices at the domestic price level and
nontradables at domestic market prices. Net financial benefits (NFB) and net
economic benefits (NEB) are expressed in domestic currency (rupees).
For the purpose of poverty impact analysis, project beneficiaries are
divided into three national groups: the poor, the nonpoor, and the government.
Net economic benefits by group are distributed between the poor and the
nonpoor, according to the extent that they benefit the poor. In the case of net
economic benefits to the government, it is assumed that 50 percent potentially
benefit the poor.
The present value of project capital costs is $25 million at border prices.
Import duties are 30 percent, the official exchange rate (OER) is Rs20/$ and
the SERF is 1.20. The market value of electricity is Rs300 million, including
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APPENDIX 1 Distribution and Poverty Impact Analysis
39
a production tax of 20 percent and there is a sales tax of 10 percent. Wages
amount to Rs80 million and the supply price of labor is 70 percent of the
average wage rate. Water sales are Rs1,000 million. The quantity of water
illegally consumed is 20 percent of revenue water. The economic cost of
water consumed and paid for is Rs1,500 million.
The NFB is equal to sales revenue of Rs1,000 million minus capital
costs of Rs650 million ($25 million multiplied by the OER of Rs20/$ plus the
import tariff of 30 percent), electricity costs of Rs330 million (the market
value of electricity plus sales tax), and labor costs of Rs80 million. The NFB
of the project shows a loss of Rs60 million in present value (see Table A1.3).
Table A1.3
) $ %&' (
'(
) *
+
Output
Capital costs
Electricity
Labor
(
1,000
(650)
(330)
(80)
#,%
0
+
NEB-NFB
Financial return
Benefits
Proportion of poor
Benefits to poor
+
)
1,800
(600)
(250)
(56)
-.
800
50
80
24
.
800
800
0.25
200
800
50
80
-
24
24
$/
130
(60)
70
0.50
35
24
954
(60)
894
24
0.333
8
243
0 +1 !./ -. 2 (!$ ! The NEB of the project expressed at the domestic price level is Rs894
million. It is equal to gross benefits of Rs1,800 million (the cost of water increased
by the proportion of water consumed but not paid for) minus capital costs of
Rs600 million (capital imports converted to local currency at the OER multiplied
by the SERF), electricity costs of Rs250 million (market value of electricity
less production tax), and labor costs of Rs56 million (wages valued at the
supply price of labor).
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
The difference between the NEB and the NFB is distributed by group.
The difference of Rs954 million is made up of (i) consumer surplus of Rs800
million (the difference between the without project cost of water and the with
project expenditure on piped water, plus the value of water consumed but
not paid for); (ii) government tax revenues from capital imports of Rs150
million; (iii) government tax revenue from electricity production of Rs80 million
(production tax of Rs50 million plus sales tax of Rs30 million); (iv) benefits to
labor of Rs24 million (wages of Rs80 million less opportunity cost of Rs56
million); and (v) loss in the economy to government of Rs100 million through
overvaluation of the exchange rate.
The NEB-NFB difference is added to the NFB by group to arrive at the
distribution of the NEB by group. The government's financial losses from
investing in the water supply project amount to Rs60 million. Adding Rs130
million in taxes results in a net economic benefit to the government of Rs70
million. Consumers gain Rs800 million in consumer surplus and laborers earn
Rs24 million more than they would have without the project. The NEB by
group is Rs894 million.
The final step is to distribute the NEB by group between the poor and
the nonpoor. One quarter of consumer surplus and one third of surplus for
labor go to those living below the poverty line. Fifty percent of the return
to the government is assumed to benefit the poor. The NEB accruing to the
poor is therefore Rs243 million. The PIR of the project is Rs243 million/Rs894
million or 27 percent.
Charges, Benefits, and the Poverty Impact Ratio
The government has decided it can no longer sustain the financial
losses of the water supply corporation. The level of water charges is to be
raised by 50 percent. It is predicted that, with a price elasticity of demand
of -0.4, this will result in a decline in the volume of revenue water of 20 percent.
Table A1.4 depicts the financial and economic returns, and the PIR, in these
new circumstances. It is assumed that capital and labor costs are fixed, while
electricity costs are fully variable. It is also assumed that nonrevenue water
will remain in the same proportion as revenue water.
The new level of charges captures some of the consumer surplus.
Financial returns become positive and substantial while economic returns,
though still positive, are reduced. The distribution of the net benefits between
groups changes significantly. The government receives less tax revenue but
now receives a surplus from the water supply corporation instead of a loss.
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APPENDIX 1 Distribution and Poverty Impact Analysis
41
Table A1.4
*+ + ,
$ %&' (
'(
) *
+
Output
Capital costs
Electricity
Labor
(
1,200
(650)
(264)
(80)
!,
0
+
Beneficiaries
NEB-NFB
Financial return
Benefits
Proportion of poor
Benefits to poor
+
1,440
(600)
(200)
(56)
-.
)
240
50
64
24
/-
240
114
206
320
0.50
160
240
0.25
60
240
50
64
!.
24
!.
$$.
24
24
0.333
8
378
206
584
228
0 +1 !!- -. 2 (/ / Its share of the benefits increase considerably. The benefits to labor remain
the same, while the benefits to consumers decrease substantially, both because
of the reduction in consumer surplus per unit of water consumed and because
of the decrease in consumption.
The PIR in these new circumstances is 39 percent instead of 27 percent.
It has increased significantly but it is not the main parameter to be affected
by the increase in charges, which has transferred more benefits to the owner
of the water supply corporation, the government. In fact, the absolute amount
of benefits going to the poor has decreased with the increase in water charge.
This suggests two things. First, the charges may have been raised by too
much; given the new financial returns, a lower increase in charges could
have ensured the financial sustainability of the corporation. Second, the tariff
structure is as important for the PIR as the tariff level. In this case the tariff
levels were increased for all types of consumers. An increase in tariff together
with a different tariff structure could have captured some of the consumer
surplus from the higher income groups while leaving the poor groups
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
unaffected. In other words, the increase in charges could be designed to leave
a higher proportion of benefits going to the poor.
By focusing attention on cost recovery mechanisms and tariff
structures, PIR analysis can help improve project design by identifying who
benefits and who pays, and by how much. Pricing policy can affect the
poverty impact of a project; it can also affect the distribution of benefits
between the private and public sectors, for example, where the water supply
corporation is privately not publicly owned. However, projects designed to
have a significant impact on the poor may at the same time have to be
provided at a different scale or in a different location, to raise the proportion
of benefits going to the poor.
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1
APPENDIX
Introduction
43
2
Benchmark Criteria for
Good Practice Project Preparation
The following is adapted from the study undertaken by a staff consultant
(Hansen 2000) who reviewed RRPs for ADB's transport projects. Generic points
in the study's final report are extracted here. Based on the review of RRPs ex
post, the report identified seven benchmark criteria that can be used to judge
whether the design of a project is such that it is likely to make a significant
contribution to poverty reduction.
The criteria are as follows:
(i) Has the project drawn on evidence about, and addressed, the
causes of poverty?
(ii) Has the project, in its choice of site and design, explicitly addressed
poverty reduction?
(iii) Has the project design been developed so as to reduce possible
adverse impacts on poor people?
(iv) Is the project linked to poverty-focused policy reforms and
institution building?
(v) Is the project designed as part of a package of integrated projects
and programs? Has the possibility that the project will crowd out
other poverty reduction projects been addressed and assessed?
(vi) Does the background data for the project establish the extent to
which the situation of the poor, in general, and that of target
groups, in particular, can be assessed?
(vii) Have poverty impact distribution assessment and benefits
incidence assessment been carried out?
Based on these criteria, the report recommends a checklist of ten
controls, for quality at entry, which would help to identify any weaknesses
and shortcomings in the design of projects, so that the design could be
corrected before it is too late in the project cycle.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
The ten controls are as follows:
(i) The project selection, design, and implementation arrangements
should incorporate key social issues and the views of major
stakeholders, as determined through a participatory process.
(ii) The social impact of the project should be disaggregated by social
group (including gender) and adequate provision made for
mitigation of any adverse impacts.
(iii) The project should be consistent with the poverty reduction
strategy as discussed in the country operational strategy (COS).
(iv) If the project is poverty-targeted, the design should be adequate
to reach the target beneficiaries.
(v) The direct impact on the poor should be clearly articulated and
quantified to the extent meaningful.
(vi) The indirect impact on the poor should be clearly articulated and
quantified to the extent meaningful.
(vii) Adequate arrangements should exist for monitoring and evaluation
of social impacts, including poverty impacts. These should include
a baseline survey, clearly specified targets, provision for data
collection on outcome indicators, and provision for financing expost evaluation of project impacts.
(viii) Project design should comply with The Bank’s Policy on Indigenous
Peoples.
(ix) Project design should comply with ADB's policy on Involuntary
Resettlement.
(x) Project design should comply with ADB's policy on cultural
proper ty.
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1
APPENDIX
Introduction
45
3
Application of Existing Primary Survey Data
in Poverty Impact Analysis at the Project Level
Fully Exploiting Existing Household Survey Data
Development assistance agencies invest considerable effort and expense
in collecting social and economic data in ADB's DMCs. Although a substantial
amount of data is collected, the lack of relevant, timely, and reliable information
is still often invoked as a constraint to conducting detailed empirical analysis
in project formulation. Lack of detailed quantitative information in areas where
projects are being implemented hinders effective targeting, and the assessment
and monitoring of the effects of poverty reduction programs and projects.
However, the potential of available data for application in poverty assessments
in support of ADB operations is significantly underexploited. The reasons why
this is the case include the high fixed costs to initially load and understand
available data, the tight time schedules typically faced in loan preparation,
and the lack of easy-to-use analytical protocols (i.e., programs for using data
needed to parameterize worksheet-based poverty analyses) to assist in the
analysis of previously collected data to support project activities. Table A3.1
describes the characteristics of surveys and censuses suitable for poverty
analysis.
The use of existing data and information for policy and project design
could be increased by facilitating the ease of access to primary data through
centralized cataloguing and storage of the data in a data archive or data bank
(Box 3.1), and development of analysis tools. More effective management and
sharing of the available data could improve the efficiency and quality of
research carried out in support of loan preparation. It assists in avoiding
duplication in data collection efforts, and minimizes the need to collect new
data. The cost and time savings from using available data rather than initiating
new data collection are other important reasons for making use of available
data in project preparation as much as possible.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A3.1
Possible Geographic Disaggregation
Recommended
Frequency of
Implementation 1
Typical Sample Size
Number of Interviews
with each Household Surveyed
Frequency of Data
Collection
,+ - $,-(
2,000–5,000 households
nationally
Multiple
Every 5 years Large agroclimatic zones/regions—usually
cannot be used at state/provincial level or
lower
* ". $*"(
2,000–5,000 households
nationally
Multiple
Annually
Large agroclimatic zones/regions—usually
cannot be used at state/provincial level or
lower
Annually
State or provincial level in some countries,
regional/national in all
Every 10
years
Village level
-+ $-(
Varies greatly depending
upon population of country
where applied
Single
*+ Entire population of the
country
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46
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5/17/02, 10:25 AM
APPENDIX
3
Application of Existing Primary Data in
Poverty Impact Analysis at the
Level
1 Project
Introduction
47
1–2 years required to carry out. High
level of expertise necessary. Very
expensive to implement.
Public Services/Infrastructure
Housing Conditions
Health/Nutrition Status
Consumption & Expenditure
Savings & Assets
Income
Education & Employment
Family Composition 4
Time, Expertise, and Resources
Required 2
Community Level Data (e.g., prices)
Topic Usually Covered 3
Other Principal Characteristics
Comprehensive survey intended for
poverty analysis. Panel data available
for some countries. Many secondary
applications (e.g., poverty maps).
Complexity of survey can raise validity
problems.
1–2 years required to carry out. High
level of expertise necessary. Expensive to
implement.
Mainly used for CPI and National
Accounts. Alone has limited application
in poverty analysis. Often supplemented
with data collection on other topics.
6–12 months required to carry out.
Moderate level of expertise necessary.
Relatively inexpensive to implement.
Quick and easy-to-implement survey that
provides poverty indicators but no or
limited information on income/
consumption. Covers many of the same
topics as LSMS but in much less detail.
About 2 years required to carry out.
Moderate-to-high level of expertise
necessary. Very expensive to conduct.
Collects little information, but covers
entire population. Data access can be
sensitive politically. Can be combined
with LSMS to generate poverty maps.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A3.1 (cont’d.)
Frequency of Data
Collection
Possible Geographic Disaggregation
Recommended
Fequency of
Implementation 1
Number of Interviews
with each Household
Surveyed
Typical Sample Size
+ * $*(
5,000 – 8,0005
Usually a
single
visit
No regular
schedule
Large agroclimatic zones/regions—usually
cannot be used at state/provincial level
) $)(
Single
Roughly 15,000
Annually
6
Generally state/provincial level
- $-(
Single
4,000 – 5,000
Every 5 years State/provincial level in some countries,
regional/national in all
1
Actual frequency of survey/census implementation varies markedly from recommendations, and often
depends upon donor support. When administered annually, RMS applies a core questionnaire each year
with rotating specialized modules. Some countries now carry out a partial-census every 5 years.
2
Particular expertise required to complete different surveys differ greatly. For example, the sample
selection and survey enumeration for the LSMS and HIES are difficult due to the complexity of the
questionnaire used, while the CWIQ has a simple questionnaire but requires expertise to identify poverty
predictors. Carrying out a census requires mapping skills and massive logistical support. DHS asks
sensitive health history questions requiring well-trained interviewers.
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APPENDIX
3
Application of Existing Primary Data in
Poverty Impact Analysis at the
Level
1 Project
Introduction
49
Public Services/Infrastructure
Housing Conditions
Health/Nutrition Status
Consumption & Expenditure
Savings & Assets
Income
Education & Employment
Family Composition 4
Time, Expertise, and Resources Required 2
Community Level Data (e.g., prices)
Topic usually covered 3
Other Principal Characteristics
About 6 months required to carry out.
High level of expertise necessary.
Relatively inexpensive to conduct.
Demographic and health profile, but
lacks economic characterization. Data
publicly available. Now coordinated
with MICS.
About 4 months required to carry out.
Moderate level of expertise necessary.
Relatively inexpensive to implement.
Quick and inexpensive to apply survey.
Requires valid poverty predictors from
LSMS survey. New type of survey
implemented in only a few countries to
date.
About 6 months required to carry out.
Relatively low level of expertise
necessary. Relatively inexpensive.
UNICEF-sponsored focusing on child
and maternal welfare information. Data
not widely disseminated.
3
4
5
6
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Darkly shaded boxes indicate topic is well covered in the survey/census, lightly shaded boxes indicate
limited data on the topic is collected.
Family composition refers to demographic characteristics such as are typically captured in an inventory
of household residents.
Sample sizes reported are rough figures. Sample used for DHS can be much larger—in the case of India,
the DHS covers about 90,000 households.
The CWIQ does not collect information on total consumption, but solicits data on easy-to-collect variables
found to be highly correlated with consumption.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Use of Household Survey Data at Different Steps of Loan Processing
At the pre-design stage
Available household survey data can provide useful input into the initial
assessment of the poverty impact of a proposed project. Data can be used
to (i) help identify beneficiaries, (ii) define the constraints and barriers faced
in increasing income and improving the welfare of the poor, (iii) assist in the
design of effective targeting mechanisms, and (iv) contribute to preliminary
project classification. Issues of data confidentiality and access are problems
that prevent use of existing data, and must be overcome in some of the DMCs
where ADB operates. In addition, a modest commitment of personnel time
and resources will be required to make available data easily accessible to
ADB staff developing projects, but an important constraint to their application
will be the readiness of staff and consultants to use the statistical and analytical
techniques needed. The availability of well-documented and standardized
Box 3.1
$ !/(
Data archives acquire, store, and disseminate data for secondary research. Secondary
research refers to research by individuals or groups besides those originally collecting
the data for research purposes that may not have been envisioned when the data were
being collected. Secondary research may seek to replicate analyses already carried out
to verify results, to extend original research, or to analyze the data for an entirely new
purpose or from an entirely new perspective. For example, DMCs periodically conduct
national censuses to measure the size, composition, and basic socioeconomic indicators
of their population. Because census data represent the sole source of survey information
at the household level which covers all localities in a country, several secondary research
techniques have been developed that combine the data from the census with other data
to consider issues beyond population and simple characterization of living conditions.
Archiving of data involves collection of information from previously conducted surveys
at the original level of observation at which the data were collected (e.g., households,
individuals, municipalities, etc.) and making it available in a data format desired by the
user. Archiving also requires documentation regarding the meaning and proper
interpretation of information collected in the surveys, along with details concerning sample
selection techniques and the extent to which data may be properly disaggregated.
Untitled-1
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APPENDIX
3
Application of Existing Primary Data in
1 Introduction
Poverty Impact Analysis at the
Project Level
51
data (to the extent possible) will foster use of the data in preliminary analysis
and description of the project environment before pre-PPTA fact-finding.
The ready availability of data at ADB can reduce time spent to collect and
process data during the mission; at the same time, it improves the quantity
and quality of the data available at this stage of loan processing. The results
of preliminary analysis could provide material for discussion with the
government agencies and stakeholders concerned during the pre-PPTA factfinding mission. By providing an empirical characterization of the targeted
beneficiaries and the environment in which they operate based on data that
were collected using proper statistical techniques, the usefulness of
discussions at the fact-finding stage could be focused and made more
operationally useful.
One particular task where using existing data could be valuable is in
carrying out the initial social assessment (ISA). These data are often suitable
for use in identifying explicit poverty reducing and social development
objectives, outcomes, and indicators. Careful consideration of what can be
gleaned from previously collected data should precede design of data collection
on poverty indicators, and the design of monitoring and evaluation strategies.
At the design stage
At the PPTA stage, there is sufficient time to carry out detailed analysis
of available data and to collect limited new information if necessary. Analysis
at this stage of project preparation can be carried out to clarify the expected
impact and effects of proposed projects on different stakeholders, including
the poor. Following in-depth analysis of existing household survey data, it is
possible to assess whether collection of additional data to fill gaps is
appropriate. New data collection may be necessary for confirming the loan
rationale, to confirm the dimensions of poverty and vulnerability in the project
service area, and to refine the identification of people affected by the project.
New data can provide information needed for subsequent monitoring and
evaluation of the project. Survey data can be used at this stage of the analysis
to estimate the poverty impact ratio (wherever project net benefits can be
valued) or project cost-effectiveness for specific groups targeted by the project,
particularly the poor.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Available Surveys Suitable for Project Level Analysis
Living Standards Measurement Surveys
The LSMS was established by the World Bank in 1980 to explore ways
of improving the quality of data on indicators of household welfare collected
by government statistical offices in developing countries. The LSMS developed
new methods for monitoring progress in raising the standards of living, and
gathered quantitative data useful in identifying the consequences of
government policies on households differentiated by their level of assets and
income, and fostered improved communication between survey statisticians,
analysts, and policymakers. The LSMS also served to increase the comparability
of welfare indicators across developing countries. The surveys gather detailed
consumption and expenditure information and a great number of quality of
life indicators needed to measure household income and assess household
welfare and changes in welfare at national and sometimes regional level.
Implementation of LSMS requires a high level of expertise in data collection
and processing, and a large commitment of resources. Typically, 2 years and
multiple visits (ranges from as few as 2, to as many as 16) to each respondent
household are required to carry out an LSMS. Sample sizes of the LSMS
conducted are included in Table A3.2, which lists household survey data sets
for ADB's DMCs that are currently available. For additional details about the
sampling procedure, instrument used, and other relevant characteristics of
the LSMS that account for the quality and higher validity of the data,
.worldbank.or
g/html/pr
dph/lsms. Grosh
readers are referred to http://www
http://www.worldbank.or
.worldbank.org/html/pr
g/html/prdph/lsms.
and Glewwe (2000) is a good source book for LSMS application to policy analysis
and potentially to project analysis.
LSMS data allow measurement of poverty in terms of household moneymetric income with sample validity at the national and in most cases regional
levels. Use of LSMS at levels of geographic disaggregation below the regional
level is generally impossible due to the relatively small sample sizes. A general
rule of thumb applied in LSMS is that a minimum of 350-400 households should
be surveyed from each state or province of a country in generating the overall
sample. The information collected in the LSMS is ideally suited for preparing
detailed country poverty profiles. The data can be potentially applied to
estimate the proportion of the poor within a project's intended clientele, and
to identify project stakeholders and the likely effects of the project on different
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APPENDIX
3
Application of Existing Primary Data in
Poverty Impact Analysis at 1
the Introduction
Project Level
53
stakeholder groups. Poverty incidence analysis and examination of the local
and household characteristics that are highly correlated with poverty status
are other common applications of the data that are useful in project preparation.
Household Income and Expenditure Surveys
The HIES (or household budget surveys) are single topic surveys aimed
at collecting detailed information on household consumption and income. Like
the LSMS, which generally includes the HIES as part of its longer
questionnaires, collection and data preparation for these surveys is expensive
and requires both time and expertise. Data are collected during multiple
interviews with the surveyed households. HIES data allow measurement of
household money-metric income and poverty status, but the principal reasons
these surveys are usually collected are for use in formulating the national
consumer price indices or to provide information needed to produce national
accounts information. Data from the surveys can sometimes be used in
analyses at the subnational level depending upon the sample size. The data
can be used to calculate headcount indices of poverty, but are of limited use
by themselves in generating poverty profiles as they neither provide information
on household characteristics expected to influence household income and
poverty status nor quality of life indicators besides income and consumption
poverty. However, it is possible to supplement HIES with data collection
modules to gather information on household characteristics and living
conditions. Such data can be used in analyses useful for project preparation
including assessment of the risk that income shocks will move households
into poverty, poverty incidence across the particular groups in the population,
and examination of the local and household characteristics that are highly
correlated with poverty status.
Rapid Monitoring Surveys (or Priority Surveys)
These surveys are similar to LSMS, but are shorter and require much
less time and fewer resources to conduct—although they are still quite costly.
Usually one visit only to each household is required to collect the information
included in the surveys. This requires survey questions to rely on respondent
recall more than other surveys that use multiple interviews with each
respondent, which increases measurement error. Sample sizes for them are
larger, so smaller subsamples (e.g., households with selected characteristics,
or surveys collected in a particular jurisdiction) can be studied and remain
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54
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
statistically valid. Rapid monitoring surveys have a more limited set of
questions to measure household incomes and expenditures than the surveys
discussed above, but yield data that allow measurement of income poverty
and provide information useful in generating a country poverty profile. Like
the LSMS and HIES, data from these surveys are suited for a number of
other analyses for poverty impact assessment (e.g., poverty risk analysis,
poverty correlation, and poverty incidence analysis).
Population and Housing Censuses
Data collected in periodic population and housing censuses carried
out in most DMCs include information on the demographic characteristics of
households as well as information about the literacy and education of
individuals and basic characterization of the economic activities and housing
conditions. These variables are useful in computing basic needs poverty
indicators. Data from the census can be used to create detailed (low scale)
maps showing the levels of basic service provision and population across all
regions of a country.
Censuses usually collect no data or very limited data on household
income, expenditure, or consumption. They cannot be used to measure
household income or its relationship to a poverty line. The crucial advantage
of census data is that they cover all households and regions of a country, so
data from the census can be analyzed at low levels of aggregation (i.e., census
data can provide valid information on local conditions about all households
in an area where a project is being prepared). Used in conjunction with data
from other surveys reviewed in this appendix, it is often possible to produce
geographically disaggregated income based poverty indicators. The
methodology for combining census and income survey data to create
comprehensive and detailed maps of income poverty incidence in a country
has been termed poverty mapping and is discussed in Box 3.2. Details are
provided in Bigman and Fofak (2001). Minot (2000) provides a case study
applied to Viet Nam.
Untitled-1
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APPENDIX
3
Application of Existing Primary Data in
Poverty Impact Analysis at 1
the Introduction
Project Level
55
Box 3.2
+ - + Poverty mapping combines survey and census data to generate poverty and inequality
profiles at low levels of aggregation.
In the past, data shortcomings have made it impossible to generate detailed poverty
and inequality profiles. On the one hand, surveys like the LSMS conducted by the World
Bank in many developing countries have solid expenditure or income data, but at low
levels of geographic aggregation they are not representative and lack sufficient sample
size to construct poverty and inequality profiles. On the other hand, the national censuses
carried out in many countries have sufficient population coverage, but do not include
high quality information on expenditure or income.
The poverty mapping methodology requires the following steps:
(i) Select all variables which exist in both the household survey and the census data
set (pay attention to variable definitions).
(ii) Use the household surveys (LSMS) to run a series of linear regressions explaining
household consumption in each region that is designed to be representative. For
these regressions, the left hand side (dependent) variable is the natural log of per
capita expenditure in each household. The right hand side variables are household
demographic variables selected by the user.
(iii) Apply the estimated coefficients from the above step to the census data to impute
a value of log per capita expenditure for each census household.
(iv) Use these imputed values to produce poverty or inequality profiles for the desired
aggregation units of the census data. The technique allows calculation of standard
errors for whichever welfare measure is estimated. This offers a means to assess
the statistical reliability of estimates as well as comparisons across estimates for
different geographic areas.
Examples of poverty maps created from this procedure include South Africa,
Guatemala, Panama, Nicaragua, Ecuador and Peru, and Brazil that led to some actual
program applications by the governments. The process of developing poverty map
results in transfer of skills and capacity necessary to refine, revise and update the method.
In the Asia-Pacific region, the methodology is being applied to Cambodia and Pakistan
and is being considered for World Bank assistance as of February 2001.
Requirements for poverty mapping:
- census and household survey that were conducted roughly in the same time period;
- at least one person who has statistical and/or econometric skills;
- 2-6 months for the whole exercise depending on quality of the data and analysts'
experience; and
- a statistical analysis package capable of estimating multiple regression models.
Sources: Demombynes (2000), Henninger (1998), Lanjouw (2001), and Ozler (2000).
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A3.2
, 0 * - $%1234&333(
(households)
Afghanistan
Azerbaijan
1989
1995
1999
Population and Housing Census
Living Standard Measurement Survey
Population and Housing Census
Bangladesh
1981
1984
1986
1989
1991
1991
1994
1995
1997
1999
2000
2001
Population and Housing Census
Household Expenditure Survey
Household Expenditure Survey
Household Expenditure Survey
Household Expenditure Survey
Population and Housing Census
Demographic and Health Survey
Household Expenditure Survey
Demographic and Health Survey
Demographic/Health Service Provision Assessment
Demographic and Health Survey
Demographic and Health Survey (special)
Bhutan
1999
Household Income and Expenditure Survey
Cambodia
1991
1993
1997
1998
1998
1999
2000
Demographic and Health Survey
Socio-Economic Survey
Socio-Economic Survey
Demographic and Health Survey (special)
Population and Housing Census
Socio-Economic Survey
Demographic and Health Survey
China, People’s Rep.
of
1982
1985
1990
1990
1992
1993
1994
1995
1995
1998
1999
Population and Housing Census
Rural/Urban Household Survey
Rural/Urban Household Survey
Population and Housing Census
Rural/Urban Household Survey
Rural/Urban Household Survey
Rural/Urban Household Survey
Rural/Urban Household Survey
Sample Population and Housing Census
Rural/Urban Household Survey
Rural/Urban Household Survey
Cook Islands
Untitled-1
56
5/17/02, 10:25 AM
n.a.
2,016
n.a.
n.a.
5,760
n.a.
9,174
7,440
8,682
350 facilities
9,854
100,000
3,854
5,578
6,000
7,630 women
n.a.
6,000
13,500 women
n.a.
90,980
102,138
n.a.
130,780
102,960
102,360
102,860
100,000
100,000
APPENDIX
3
Application of Existing Primary Data in
1 Introduction
Poverty Impact Analysis at the
Project Level
Fiji Islands
Untitled-1
Hong Kong, China
1981
1986
1991
1994
1996
Population and Housing Census
Sample Population and Housing Census
Population and Housing Census
Household Expenditure Survey
Sample Population and Housing Census
n.a.
n.a.
5,591
-
India
1981
series
1991
1993
1997
1999
Population and Housing Census
National Sample Survey
Population and Housing Census
Demographic and Health Survey
UP-BIHAR Survey of Living Conditions
Demographic and Health Survey
n.a.
25,000
n.a.
88,562
2,250
97,282
Indonesia
1980
1980
1981
1982
1983
1984
1985
1986
1987
1987
1988
1989
1990
1990
1991
1992
1993
1993
1994
1994
1995
1996
1997
1997
1997
1997
1998
1998
1998
1998
1999
1999
SUSENAS - Indonesia's Socio-Economic Survey
Population and Housing Census
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
Demographic and Health Survey
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
Population and Housing Census
Demographic and Health Survey
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
Indonesian Family Life Survey (IFLS I)
SUSENAS - Indonesia's Socio-Economic Survey
Demographic and Health Survey
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
SUSENAS - Indonesia's Socio-Economic Survey
Demographic and Health Survey
100 Village Survey I
Indonesian Family Life Survey (IFLS II)
100 Village Survey II
100 Village Survey III
SUSENAS - Indonesia's Socio-Economic Survey
Indonesian Family Life Survey (IFLS II+)
100 Village Survey IV
SUSENAS - Indonesia's Socio-Economic Survey
57
5/17/02, 10:25 AM
n.a.
50,000
60,000
14,142
46,000
n.a.
26,858
65,600
7,224
33,738
65,664
65,664
34,255
12,000
7,224
12,000
12,000
65,664
2,000
12,000
205,000
57
58
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A3.2 (cont’d.)
, 0 * - $%1234&333(
Kazakhstan
Series
1989
1995
1996
1999
1999
Family Budget Survey
Population and Housing Census
Demographic and Health Survey
Living Standards Measurement Survey
Population and Housing Census
Demographic and Health Survey
n.a.
4,178
2,000
n.a.
5,844
Kiribati
Korea, Rep. of
1963-2000
1985
1995
1996
Kyrgyz Republic
Untitled-1
1989
1993
1996
1996
1997
1997
1999
1999
Quarterly Income and Expenditure Survey
Population and Housing Census
Population and Housing Census
National Survey of Family Income and Expenditure
5,200
n.a.
n.a.
30,000
Population and Housing Census
Poverty Monitoring Survey
Poverty Monitoring Survey (Spring)
Poverty Monitoring Survey (Fall)
Poverty Monitoring Survey
Demographic and Health Survey
Poverty Monitoring Survey
Population and Housing Census
n.a.
2,100
2,000
2,000
2,000
3,672
n.a.
n.a.
2,937
2,869
n.a.
8,882
Lao PDR
1985
1992
1993
1995
1997-1998
Population and Housing Census
Lao Expenditure and Consumption Survey I
Social Indicators Survey
Population and Housing Census
Lao Expenditure and Consumption Survey II
Malaysia
1973
1976-1977
1980
1984
1987
1988-1989
1989
1991
1991
1995
1997
1999
Household Expenditure Survey
Malaysian Family Life Survey I
Population and Housing Census
Household Income/Basic Amenities
Household Income Survey
Malaysian Family Life Survey II
Household Income/Basic Amenities
Household Income/Basic Amenities
Population and Housing Census
Household Income/Basic Amenities
Household Income/Basic Amenities
Household Income/Basic Amenities
Survey
Survey
Survey
Survey
Survey
Survey
1,262
n.a.
12,000
926
12,000
n.a.
39,139
-
Maldives
1985
1990
1995
1997
Population and Housing Census
Population and Housing Census
Population and Housing Census
Vulnerability and Poverty Survey
n.a.
n.a.
n.a.
2,600
Marshall Islands
1999
Population and Housing Census
n.a.
Micronesia, Fed.
States of
2000
Income and Expenditure Survey
Population and Housing Census
n.a.
58
5/17/02, 10:25 AM
APPENDIX
Mongolia
1989
1992-1999
1995
1999
1999-2000
Myanmar
1989
1997
3
Application of Existing Primary Data in
Poverty Impact Analysis at the
Project Level
1 Introduction
59
Population and Housing Census
Monthly Income and Expenditure Survey
Household Survey
Household Survey
Population and Housing Census
n.a.
1,500
n.a.
Household Income and Expenditure Survey
Household Income and Expenditure Survey
25,470
Nauru
Untitled-1
Nepal
1981
1984
1987
1991
1991
1995-1996
1995-1996
1996
2001
Population and Housing Census
Multipurpose Household Budget Survey
Demographic and Health Survey
Population and Housing Census
Rural Credit Survey
Nepal Living Standards Survey
Arun Living Standard Survey
Demographic and Health Survey
Demographic and Health Survey
n.a.
5,323
4,709
7,336
3,388
1,200
8,082
-
Pakistan
1981
1984
1986-1991
1987
1990
1991
1991
1995
1996
1996
1998
Population and Housing Census
Household Integrated Economic Survey
Panel Survey
Household Integrated Economic Survey
Household Integrated Economic Survey
Household Integrated Economic Survey
Demographic and Health Survey
Household Integrated Economic Survey
Household Integrated Economic Survey
Child Labor Survey
Population and Housing Census
n.a.
12,000
800
12,000
12,000
4,800
7,193
12,500
12,500
n.a.
Papua New Guinea
1990
1996
Urban Household Expenditure Survey
Living Standards Household Survey
Philippines
1980
1985
1988
1990
1991
1993
1994
1995
1995
1997
1998
1998
1999
2000
Population and Housing Census
Family Income and Expenditure Survey
Family Income and Expenditure Survey
Population and Housing Census
Family Income and Expenditure Survey
Demographic and Health Survey
Family Income and Expenditure Survey
Population and Housing Census
Survey on Children 5-17 Years Old (SCL)
Family Income and Expenditure Survey
Demographic and Health Survey
Annual Poverty Indicators Survey
Annual Poverty Indicators Survey
Population and Housing Census
59
5/17/02, 10:25 AM
1,200
1,146
n.a.
n.a.
24,789
12,995
24,797
n.a.
41,000
12,407
41,000
n.a.
60
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A3.2 (cont’d.)
, 0 * - $%1234&333(
Samoa
n.a.
n.a.
7,000
-
1980
1990
1992
1995
Population and Housing Census
Population and Housing Census
Household Expenditure Survey
General Household Survey
Sri Lanka
1981
1985
1987
1990
1995
1996
1999
Population and Housing Census
Labor Force and Socio-Economic Survey
Demographic and Health Survey
Household Income and Expenditure Survey
Socio-Economic Survey
Consumer Finance and Socio-Economic Survey
Child Labor Survey
Taipei,China
1980
1990
Population and Housing Census
Population and Housing Census
n.a.
n.a.
Tajikistan
1989
1999
2000
Population and Housing Census
Living Standards Survey
Population and Housing Census
n.a.
2,041
n.a.
Thailand
1908
1981
1986
1987
1988
1990
1990
1992
1994
1996
1998
Population and Housing Census
Thailand Socio-Economic Survey
Thailand Socio-Economic Survey
Demographic and Health Survey
Thailand Socio-Economic Survey
Population and Housing Census
Thailand Socio-Economic Survey
Thailand Socio-Economic Survey
Thailand Socio-Economic Survey
Thailand Socio-Economic Survey
Thailand Socio-Economic Survey
9,045
16,272
31,143
-
1997
1998
2000
Living Conditions Survey
Living Standards Measurement Survey
Demographic and Health Survey
Singapore
Solomon Islands
n.a.
19,498
7,669
36,352
14,000
10,000
-
Tonga
Turkmenistan
Untitled-1
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5/17/02, 10:25 AM
2,000
2,350
6,303
APPENDIX
3
Application of Existing Primary Data in
1 Project
Introduction
Poverty Impact Analysis at the
Level
Tuvalu
Uzbekistan
1989
series
1996
Population and Housing Census
Family Budget Survey (annual since 1950)
Demographic and Health Survey
n.a.
3,703
1989
1992
1997
1998
series
1999
Population and Housing Census
Viet Nam Living Standards Survey
Demographic and Health Survey
Viet Nam Living Standards Survey
Multi-Purpose Household Survey (annual)
Population and Housing Census
n.a.
4,800
7,001
6,000
45,000
n.a.
Vanuatu
Viet Nam
Notes:
n.a. – not applicable.
Sources:
Socio-Economic Surveys: World Bank, Poverty Monitoring Database (On-line).
Available: Demographic and Health Surveys: Macro International Inc. (On-line).
Available: !
Child Labour Surveys: International Labour Organisation (On-line).
Available: !
!!
Population and Housing Census: US Census Bureau (On-line).
Available: !
!!
!
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61
62
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Poverty maps provide spatially disaggregated measures of incomebased poverty, thus overcoming one of the chief shortcomings of LSMS,
HIES, and the other large surveys. These generally rely on samples that are
small relative to the population, so their results are not amenable to
partitioning as required to study poverty in a particular locality. With the
poverty maps and the data generated in their construction, staff gain access
to sufficient data on income-poverty at the household level in a given locality
(i.e., the locality where a project is being considered) to characterize and
analyze poverty at the local level. Poverty maps provide useful information
for geographic targeting and monitoring of poverty reduction projects.
Development of poverty mapping methodology at the World Bank was
one of many poverty assessment initiatives, but application of the output from
poverty maps in PPTA work in ADB's DMCs is largely untested. It is also
uncertain at this point whether it will be cost effective to gather the information,
carry out the programming, and gather related resources needed for ADB to
generate poverty maps in its DMCs. Accessing primary data from national
censuses may represent another serious barrier to generating poverty maps
that must be overcome in some DMCs. Accordingly, pilot testing of the
methodology as part of an ongoing country poverty analysis or economic
and sector work, in consultation with the World Bank, is recommended.
Demographic and Health Surveys
DHS are nationally representative household surveys that provide data
on a wide range of monitoring and impact evaluation indicators in the areas
of population, health, and in some cases nutrition. DHS collect information
on assets ownership that can be used as proxies for inferring levels of household
income and wealth, but does not allow direct income-poverty measurement
(see Box 3.3). Calculated wealth indices can be used to classify households
into wealth quintiles. However, it is not appropriate to compare such wealth
indices to absolute poverty lines, so they are not suitable for poverty
measurement. DHS may be used for producing poverty maps, based on
“relative” poverty measure. The permissible level of spatial disaggregation
of such maps depends on the sample size of the DHS. Other poverty analyses
that have used DHS data are: poverty risk analysis; generating a poverty profile
(i.e., analyzing the relationship of wealth quintile with levels of education,
school enrollment, health outcomes, basic service access, basic labor market
characteristics); and poverty incidence analysis (i.e., distribution of health,
Untitled-1
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the Introduction
Project Level
63
Box 3.3
+ ) + * Using DHS data, the World Bank produced country reports on health, nutrition, population
(HNP) and poverty, which provide statistics concerning intra-country differences between
rich and poor with respect to HNP service and status use. The method has been applied
to Bangladesh, India, Kazakhstan, Kyrgyz Republic, Nepal, Pakistan, Philippines, Uzbekistan,
and Viet Nam.
Socioeconomic status is defined in terms of assets or wealth, rather than in terms
of income or consumption. Each household asset for which information was collected
through the DHS was assigned a weight or factor score generated through principal
components analysis. The resulting asset scores were standardized in relation to a standard
normal distribution with a mean of zero and a standard deviation of one. These standardized
scores were then used to create the break points that define wealth quintiles as follows.
Each household was assigned a standardized score for each asset, where the score
differed depending on whether or not the household owned that asset (or, in the case
of sleeping arrangements, the number of people per room). These scores were summed
by household, and individuals were ranked according to the total score of the household
in which they resided. The sample was then divided into population quintiles—five groups
with the same number of individuals in each.
The method has been applied to Bangladesh, India, Kazakhstan, Kyrgyz Republic,
Nepal, Pakistan, Philippines, Uzbekistan, and Viet Nam.
The resulting figures describe the HNP status and service use among individuals
belonging to different socioeconomic classes. The figures are intended to provide World
Bank operational staff, the government officials with whom they work, and others with
basic information for use in preparing country analyses and in developing HNP activities
for the disadvantaged.
Source: -.//0000
//--//
1-
education, specific program spending by area, and households’ wealth
.measur
edhs.com.
quintiles). For more information see www
www.measur
.measuredhs.com.
Core Welfare Indicators Questionnaire Surveys
Developed jointly by the World Bank (Africa Region) in collaboration
with UNDP and UNICEF, the CWIQ is a short and relatively easy to administer
survey that monitors social indicators on an annual basis. The CWIQ is designed
to complement rather than replace other more intensive surveys (Box 3.4).
The CWIQ does not try to collect data on household income or expenditure.
As a result, the CWIQ may not be used for money-metric measurement of
poverty. Household well-being is assessed using poverty predictors, which
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Box 3.4
+ )
The poverty predictors are selected from a host of variables constructed from an
existing database. The poverty predictors are constructed under the assumption that
prior to the implementation of the CWIQ, a more comprehensive household survey
(e.g., LSMS) has already been carried out in the country. It would also be desirable for
a participatory study on poverty analysis to have been constructed in the country to
provide some indication on the determinants of poverty and their variation across regions.
A combination of information from these two different sources—qualitative studies
and more comprehensive household survey—is used to identify the set of explanatory
variables that are closely correlated to household aggregated total expenditure. A maximum
number of potential explanatory variables, and the predicted variable of interest (aggregated
total household or expenditure) are constructed at this stage of the process.
A regression model will be built with the natural logarithm of the total household
expenditure (or income) as the dependent variable. The first task is to carry out a multiple
correlation analysis assessing the magnitude of correlation between the potential regressors
and the dependent variable. The multiple correlation analysis identifies a host of variables
that are highly correlated with the dependent variable. Further reduction of the
dimensionality of the model is attempted using stepwise procedure in the regression
analysis setting (gradually dropping explanatory variables with less predictive power).
This iterative process of gradual elimination allows one to reduce the number of
predictors to a core set of variables that can be collected easily. For practical reasons,
it is suggested that the core set of poverty predictors could well be limited to 10 variables
as well.
Source: World Bank (August 1999, Annex 4).
are variables from the larger survey that are strongly correlated with poverty
status and are easy to collect. For example, analysis of the larger survey
may indicate a strong correlation between the level of education of the
household head, tractor ownership, and ease of access to health services,
and the household’s poverty status. The CWIQ would then collect information
on these household characteristics.
CWIQ survey data are well suited for monitoring achievements in
development objectives defined through leading indicators (e.g., indicators
that give advance warning of a future impact such as household access and
use of public services). The CWIQ can be used for estimating the proportion
of the poor—as defined by poverty predictors—within the project's intended
clientele, and to identify the likely effects of the project on different beneficiaries.
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APPENDIX
3
Application of Existing Primary Data in
Poverty Impact Analysis at 1
the Introduction
Project Level
65
CWIQ surveys can be carried out as national surveys, but are primarily
designed for use at a more limited geographic scale to collect data needed
for project monitoring and evaluation. It does not attempt to measure the
impact of projects on living standards, but contributes to monitor outcome
indicators (e.g., access, usage, and satisfaction with basic services provided).
CWIQ surveys collect indicators of household well-being and indicators of
household access/usage/satisfaction with community and other basic services.
Other features include: large sample sizes, a short questionnaire that can be
administered quickly during a single visit to each respondent household, quick
data entry and validation, and simple reporting of results. Additional
information about the CWIQ is available on-line at http://www4.worldbank.org/
afr/stats/cwiq.cfm.
Multiple Indicator Cluster Surveys
MICS have been developed by UNICEF to be low-cost household surveys
that quickly generate data on key welfare indicators inadequately monitored
in other data collection systems. MICS have been implemented in more than
100 countries since 1995. UNICEF, with partner agencies, defined a set of
indicators to guide the assessment process (UNICEF 1995). Indicators include
measures meant to monitor progress in established health, education and
welfare, and new indicators intended to fill gaps in information concerning
the welfare of children in developing countries (e.g., child labor, birth
registration, disability, orphans/alternative family care, and early child
development). In collaboration with a number of other agencies, UNICEF
has harmonized the MICS with other major survey programs (e.g., USAIDsponsored demographic and health surveys) to generate comparable and
complementary data across countries. MICS data does not allow measurement
of poverty based on income (money-metric), but they can be used to generate
population profiles regarding health, education and child labor indicators
usable as proxy variables of poverty status.
Collecting Project-Specific Primary Data
If after reviewing available data, ADB staff find that additional data are
required to provide the information necessary for project design, project
implementation, or the ex-ante or ex-post assessment of a project's effects on
poverty, primary data collection is appropriate. ADB can support data
collection through technical assistance activities.
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ADB development of new instruments and procedures specific for its
projects should proceed from the established surveys discussed in this
appendix and focus on priority topics. In such efforts, ADB would be well
advised to rely on existing questionnaires (e.g., LSMS) and methodologies
(e.g., CWIQ survey development or poverty mapping), as these have been
widely applied, tested, and validated. Where LSMS or HIES are available,
the CWIQ provides a good model for how to develop a well focused and easyto-collect survey that provides basic poverty measures based on validated
proxies/predictors. Existing surveys can be supplemented with modules to
address project-specific concerns such as measuring user willingness-to-pay
for services, identification of priorities by the population, time use, access to
services, assets ownership, and similar information.
In countries where such data exist, it may be sufficient to collect limited
additional data, specific to the project's requirement. Well-focused and easy
to administer surveys can be conducted to provide baseline and follow-up
indicators. Several aid agencies have developed data collection instruments
specific to their targeting and monitoring needs: UNICEF developed the MICS,
ILO the Statistical Information and Monitoring Programme on Child Labour
(SIMPOC), the World Bank the LSMS and the CWIQ, USAID (through Macro
International Inc.) the DHS and service provision assessment surveys, among
others.
In countries where no reasonably recent data on poverty are available,
implementation of a large LSMS or HIES is likely necessary to generate basic
measures of income and money-metric poverty in the country. Such information
is essential for setting poverty lines and producing a poverty profile. The
expense and the commitment of time and resources to carry out such surveys
preclude their implementation for a particular project.
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APPENDIX
4
Case Illustrations of Distribution Analysis
The following two cases illustrate the application of distribution analysis.
The first from Mongolia is based on an actual report and recommendation of
the President (RRP). The second from Bangladesh is a research study
commissioned by EDRC and based on the original RRP appraisal. In both
cases the distribution analysis provides a starting point for poverty impact
assessments. It should be noted that the purpose of the illustrations here is
primarily to demonstrate that there have been actual cases of application to
ADB projects. They do not necessarily represent best practices. Project analysts
are encouraged to explore improvement on the practice, for example, by
incorporating the project financing aspect illustrated in Appendix 5.
Case 1– Mongolia: Energy Conservation Project
The following is a rework of the financial and economic analysis
presented in the ADB RRP on a loan to Mongolia for the Energy Conservation
Project. 1 Only the relevant information on financial and economic analysis is
drawn from the document. To simplify the exposition a few amendments have
been made but the analysis is basically set out in the RRP. A similar analysis
can be found in the RRP for the Ulaanbaatar Heat Efficiency Project. 2
Project Description
Ulaanbaatar is the coldest capital in the world. Because of its harsh
climate, heat is a basic human need. However, a serious deterioration in the
efficiency and reliability of heat and electricity supply is affecting the people's
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livelihood. The heating system was put into operation in 1953 and much of
the piping has reached the end of its technical life. Because of leaks in the
system, there are huge losses of hot water, estimated at 4.1 million tons per
year. Also, following the collapse of the power system in February 1996,
frequent electricity outages are adversely affecting industry, commercial
enterprises, and also residential consumers who suffer lack of lighting and
are unable to use appliances during peak periods. There are only limited backup
facilities installed in hospitals and other essential services. The objectives of
the Project are to (i) improve district heating reliability and reduce losses by
rehabilitating critical sections of the district heating system in Ulaanbaatar;
(ii) encourage end-use energy conservation through improved metering and
through demonstration projects; and (iii) improve district heating system
operation and maintenance through on-the-job training and technical support.
The Project is expected to reduce district heating losses by an average of 11
percent (equivalent to 192 GWh/year) and electricity losses by 8 percent
(equivalent to 30 GWh/year). The total project cost of $13.19 million will be
financed by ADB ($10 million) and the Government ($3.19 million).
Least-Cost Analysis
There is no practical alternative to the Project given the urgent need for
more efficient and reliable electricity and heat supply and the financial constraints
on building new Energy Authority (EA) facilities. On an incremental basis, the
Project will save both heat and electricity at a lower per unit cost ($6.1 per Gcal
and $0.013 per kWh, respectively) compared with generating heat and electricity
from a new plant ($9.8 per Gcal and $0.046 per kWh, respectively).
Financial and Economic Analysis
All calculations are in US dollars and the assumptions used in the
financial and economic analysis are as follows.
(i) Project life of 20 years.
(ii) An estimated cost (inclusive of physical contingency) of $11.8
million at constant prices.
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APPENDIX 4 Case Illustrations of Distribution
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1 Introduction
69
(iii) Reduced district heat loss savings due to rehabilitated piping (heat
loss reduction in distribution) and metering/demonstration projects
(reduced consumer wastage), respectively, and reduced electricity
losses due to more efficient heating pumps (technical loss
reduction) and due to transmission and substation metering (nontechnical loss reduction). The savings involved are set out in Table
A4.1.
(iv) Financial prices of heat and electricity are $7.3 per Gcal and $0.059
per kWh, respectively. These are prices current at the time of
planning the project adjusted for real tariff movements in future
years.
(v) Income tax of 40 percent is applied to the net positive cash flows
after allowance of 5 percent depreciation on the capital cost.
(vi) Economic prices of heat and power are determined by estimated
willingness to pay. Based on an upper bound of $38.4 per Gcal
associated with the observed usage of electric bar radiators and
a lower bound of $2.7 per Gcal which is the average price paid
for apartment central heating, and a curvature correction factor
of 0.3, the willingness to pay for heat is estimated at $13.4 per
Gcal. The real heat tariff is 54 percent of this figure. Similarly,
based on an upper bound of $0.12 per kWh for diesel generation
and a lower bound of $0.033 per kWh which is the present average
power tariff, and a curvature correction factor of 0.3, willingness
to pay for electricity is estimated at $0.059 per kWh. Reduced
technical electricity losses are valued at this economic price, but
reduced nontechnical losses due to better metering are given no
value in the economic analysis as they represent a transfer
between EA and users who had previously been nonmetered. It
is assumed in the RRP that the real electricity tariff will be increased
to the willingness to pay for electricity.
(vii) A standard conversion factor (SCF) of 1.0 is used for Mongolia.
This is almost certainly a simplification, which is not pursued here,
as the purpose of this appendix is to illustrate distribution and
poverty analysis. However, use of an SCF of unity has two
important implications; first since no economic adjustments are
made to project costs use of resources in constructing the project
creates no distributional effects and second the analysis is identical
whether one uses a domestic or a world price numeraire. Hence
these two sources of complication are removed in this case.
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FIRR and EIRR calculations are shown in Table A4.1. FIRR at 16 percent
was considered financially viable at appraisal without the benefit of an
estimated financial opportunity cost of capital. The weighted average cost of
Project capital was considered difficult, given the variable economic conditions
prevailing in Mongolia and lack of an indicative set of interest rates and rates
of return at the time of appraisal. EIRR at 38 percent, being comfortably higher
than the benchmark 12 percent, was considered economically viable.
Table A4.1
# " "+ ($ million)
! %
#6
Heat loss reduction
Consumer heat wastage reduction
Technical electricity loss reduction
Nontechnical electricity loss reduction
%
4.69
5.10
9.87
4.94
0.44
0.47
1.18
0.59
0.58
0.63
1.18
0.59
0.58
0.63
1.18
0.59
11.80
5.10
5.18
11.80
0.47
0.00
(9.12)
0.00
0.63
0.70
2.27
0.00
0.63
0.70
2.27
8.60
9.36
9.87
0.00
0.80
0.87
1.18
0.00
1.05
1.15
1.18
0.00
1.05
1.15
1.18
0.00
"
Capital costs
Lost consumer heat revenue
Profits tax
11.80
0.00
0.00
11.80
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
"!
Economic NPV
Economic IRR (%)
(8.94)
3.39
3.39
16.03
38.00
#76
Capital cost
Lost consumer heat revenue
Profits tax
Net financial flow
Financial NPV
Financial IRR (%)
"!
Heat loss reduction
Consumer heat wastage reduction
Technical electricity loss reduction
Nontechnical electricity loss reduction
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5
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2.52
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APPENDIX 4 Case Illustrations of Distribution
Analysis
71
71
Distribution Analysis
It should be noted that unlike the general case in which the domestic
price numeraire should be used in distribution analysis, the world price
numeraire in dollar unit was used for this project as the cost estimate was
done in US dollars. However, as we have noted, the use of an SCF of 1.0 removes
the problem associated with applying distribution analysis to appraisals at
world prices (since by implication the average divergence between world
and domestic prices is zero).
Using the results in Table A4.1 and following the procedure explained
in this paper, we can lay out the distribution of benefits among the basic
stakeholders: Energy Authority (EA) as the operating entity, consumers, and
the Government. The result is in Table A4.2 showing how the net economic
benefits accrue to each stakeholder category. A discount rate of 12 percent
is used to calculate net present values of both financial and economic flows.
EA gains the financial NPV of the project of $2.52 million. This gain arises
from the savings in heat and power losses valued at the tariffs minus the
investment cost of the project and taxes paid on operating profits. Heat wastage
savings to consumers are not a gain to EA and hence in the financial NPV for
presentational purposes—these are shown twice as part of total benefits and
as part of costs (in parenthesis) with the two values balancing out. The economic
NPV exceeds the financial NPV by $13.51 million. Out of this, $5.18 million
goes to the Government through profits tax with the remaining $8.34 million
going to consumers. The consumers benefit from the fact that they will receive
heat at a tariff that is below their willingness to pay. The tariff is approximately
54 percent of willingness to pay, hence for heat loss reduction consumer gains
are roughly 46 percent of the economic value of heat. For reductions in
consumer heat wastage, consumers gain the energy saved valued at their
willingness to pay, although as they will now buy less from the EA as a result
of these savings the EA will lose the energy involved valued at the heat tariff.
Reductions in technical power losses bring no net gain to consumers as by
assumption their willingness to pay equals the tariff. For nontechnical power
losses due to improved metering consumers must pay more while the EA
gains this additional revenue, so there is no net economic effect. Part of this
transfer will circulate back to the Government through the profits tax paid by
the EA. No one loses from the Project.
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Table A4.2
-+ "+ ($ million)
PV
(1)
Financial
Incremental benefits
Heat loss reduction
Consumer heat wastage
Technical electricity loss
Nontechnical electricity loss
Incremental costs
Capital costs
Lost consumer heat revenue
Profits tax
Net Benefits
Gains and losses
(2)
Economic
(2) - (1)
Difference
4.69
5.10
9.87
4.94
8.60
9.36
9.87
0.00
3.91
4.26
0.00
(4.94)
(11.80)
(5.10)
(5.18)
(11.80)
0.00
0.00
0.00
5.10
5.18
2.52
16.03
13.51
EA
Distribution
Gov't.
Consumers
3.91
9.36
(5.10)
(4.94)
5.10
5.18
0
2.52
5.18
5.18
8.33
8.33
Notes:
1. EA gains financial NPV.
2. Gov't. gains Profits tax.
3. Consumers gain: — difference between heat loss reduction valued at willingness to pay
($13.4/Gcal) and heat loss reduction valued at heat tariff ($7.3/Gcal)
— saving in consumer heat wastage valued at heat tariff ($7.3/Gcal)
4. Consumers lose: — reduction in nontechnical losses due to improved metering valued at power
tariff ($0.59/kWh).
The result here indicates that the distributional issue does not pose a
problem in selecting the Project as there will be no losers and no need for
compensation for any party. If we were to proceed further to carry out poverty
impact analysis, as explained in the text, further assumptions on the proportion
of the poor within each stakeholder category would have to be made. Doing
so for the benefits going to the consumers would be relatively straightforward
as data for customer use should be available at the Energy Authority. But for
the benefits accruing to the Government the issue is more problematic.
However, the general point is that the original RRP was able to take the first
step in accurately estimating the project's distribution impact. A poverty profile
of power consumers would have allowed the consumer category to be split
between the poor and nonpoor. An example of an integrated distribution and
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1 Introduction
APPENDIX 4 Case Illustrations of Distribution
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73
poverty impact analysis for a power sector from a more recent project is given
in Case 1 in Appendix 6.
Simplification in Financial Analysis
The format used in Table A4.1 follows conventional ADB practice in
financial calculations. This involves estimating financial inflows and outflows
and deducting profits tax to give a net-of-tax FIRR. While this approach is
often adequate for most conventional appraisals it is not strictly what is
required for distribution analysis. This is because it makes no allowance for
the way the project is financed. The FIRR is a net-of-tax return to total capital
rather than a return to the equity funds committed by project owners. If the
financing plan of a project is known then for purposes of distribution analysis
the financial inflows should include the loan receipts and the financial outflows
should include loan principal and interest repayments. However, it should be
noted that if the loan is at the same real interest rate as the discount rate for
the project (i.e., if there is no subsidy component) then the present value of
the loan receipts and the present value of the sum of loan repayments should
be equal, provided there is no grace period. Hence under this situation the
omission of these loan flows, as in Table A4.1, will make no difference to the
result of the distribution analysis. However, if the loan real interest rate is
below the discount rate, the gain to owners (i.e., the return to equity) will be
greater than the return to the project as a whole and there will be transfer
between lenders and the owners of the project, which will be relevant in
distribution analysis.
A further complication arises because if the financing arrangements
for a project are not allowed for in the financial analysis the true liability for
Profits tax cannot be established. This is because in the tax regulations of
most countries interest payments as well as depreciation allowances are costs
that are allowed as deductions against tax. Therefore, omission of interest
payments will lead to an overstatement of the tax liability of owners and hence
to an underestimate of their gains from the project. The financial NPV of a
project, as currently calculated in ADB appraisals and illustrated in Table A4.1,
may thus not fully reflect the change in income for project owners.
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Case 2– Bangladesh: Jamuna Bridge Project
The following is taken from an unpublished EDRC report. The information
and data used in the paper are based on the ADB Appraisal Report on a loan
to Bangladesh for the Jamuna Bridge Project. 3
Project Description
The Jamuna, the Meghna and the Padma constitute a system of rivers
which physically divides Bangladesh into East, Southeast, and Northeast
regions. The existing transport services such as ferries and boats going across
the Jamuna River face numerous problems: a great amount of silt brought by
excessive water flow during the monsoon season; riverbank erosions; often
too narrow navigation channels; deteriorating fleet conditions due to the lack
of maintenance; and poor parking facilities at jetties. The whole ferry system
has reached its capacity limits. The Project is designed to provide a permanent,
nonmanagement intensive crossing under all-weather conditions for the
existing and potential East-Northwest traffic. It includes the following
components: (i) a 4.8 km-long and 18.5 m-wide bridge to carry four road lanes
with sidewalks; (ii) two viaducts, about 128 m-high; (iii) two guide bunds,
about 2.2 km each and a flood protection bund on the east bank; (iv) two
approach roads, about 16 km to the east and 14 km to the west (two-lane
single carriageway with paved shoulders); (v) measures to mitigate the project's
environmental impact; (vi) implementation of a resettlement plan; and (vii)
technical assistance for project management. The bridge site, 7 km south of
Sirajganj, was selected after extensive studies of 10 potential sites. In-depth
studies of the selected site were carried out to determine the optimal design
of the bridge and the required embankments. The total project cost of $696
million was to be co-financed by ADB, Overseas Economic Cooperation Fund
(Japan), International Development Association (World Bank), and the
Government.
3
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75
Financial and Economic Analysis
The project life is considered to be 50 years after the bridge opens to
traffic in 1998 (scheduled at the time of appraisal). On the basis of the past
traffic growth trends and taking into account population and per capita GDP
growth rates as well as estimated price and income elasticity of demand for
transport services, the annual traffic growth rates from 1993 to 1998 are
estimated at 6.6 percent for buses and trucks, and 8.2 percent for light vehicles.
Based on these data, the traffic diverted to the Jamuna bridge in 1998 was
projected for the three categories of vehicles. The bridge traffic is estimated
to grow at 5 percent per year during 1998–2025 and no increase thereafter
as the bridge capacity would be fully exhausted by 2025. The reduction in
waiting time and vehicle operating costs would generate additional passenger
and freight traffic on the bridge. The base year (1998) newly generated traffic
was estimated based on the price elasticity of demand for transport services
(-1.0 for light vehicles, -1.5 for buses, and -0.6 for trucks, respectively). The
incremental generated traffic for the three types of vehicles is assumed to
build up gradually from 20 percent of the total volume in 1998 to 40 percent,
50 percent, 60 percent, 70 percent, 80 percent, 90 percent, and finally 100
percent in 2005; thereafter the growth rate of the incremental traffic is assumed
to be the same as the diverted traffic.
Financial revenue for the bridge is based on the toll revenues from
diverted and newly generated traffic, and electricity interconnector fees. Toll
tariffs for the three types of vehicles are set at half the current ferry tariff,
respectively. It is assumed that the nominal tariff rates are increased every
year to remain constant in real terms. Financial costs are the investment cost
plus operating and maintenance costs of the bridge. The financial NPV is
positive, showing financial viability of the Project. Sensitivity test of the financial
NPV shows that it becomes negative if the tariff rate is reduced below 30
percent of the current ferry rate, or if the vehicle traffic growth rate goes
below 2 percent.
Economic benefits of the Project include (i) the savings in vehicle
operating costs (VOCs) gained by the diverted traffic; (ii) the value of time
saved for existing passenger and freight traffic; (iii) the benefits received by
the generated traffic (equal to the gain in consumer surplus plus the financial
toll revenue); (iv) the value of the investment saved by not constructing a
stand-alone power interconnector; (v) the value of the investment, operation
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and maintenance saved by not improving the current ferry system; (vi) the
value of the increase in truck waiting time saved from year 2000 onwards by
not operating the current ferry system; (vii) the environmental benefits of
preventing embankment erosion in areas close to the bridge and increasing
agricultural production during the monsoon season.
The estimates for VOC by kind of vehicles are based on the Bangladesh
Road Master Plan Study (1992) and the Nalka-Bonpara Road Feasibility Study
developed by the China International Engineering Consulting Corporation
(1993). The estimates of economic savings on passenger trips are based on
the individuals' hourly income. The analysis followed the practice of the Indian
Road Manual: valuing business travelers' time at 110 percent of their hourly
income, the time of the remaining economically active passengers at 50 percent
of their hourly income, and the time of nonbusiness travelers at 25 percent
of their hourly income. The hourly income of each of these three groups was
estimated using the Bangladesh Household Survey for 1985–86 updated to
1993 price levels by CPI and the annual growth rate of 1.7 percent in per
capita GDP observed in the period of 1968–1990. The time savings for freight
traffic is reflected in the reduction of inventory costs, losses, and damages
(especially to perishable agricultural goods). Because of unavailability of reliable
data, these indirect time-related costs are assumed to be equal to 10 percent
of VOC. The direct benefits accruing to the diverted traffic are estimated by
multiplying the unit savings in VOC and waiting time costs for the types of
vehicles by their corresponding traffic level. The gain in consumer surplus to
the newly generated traffic is obtained by multiplying half the unit differential
of total cost per type of vehicles by their corresponding incremental traffic.
The operating costs saved by type of vehicle due to the Project are then
adjusted by their corresponding conversion factors (weighted averages of
conversion factors of the VOC components) in order to get the real resource
value of the savings and increase in consumer surplus. The time savings are
not adjusted as labor is assumed to receive its market price (SWRF = 1).
The real economic values of the investment cost items are adjusted
using appropriate conversion factors. The shadow exchange rate factor of
1.304 (based on the 1993 EDRC shadow pricing study for Bangladesh,
Indonesia and Philippines (Jenkins 1993) is used for converting the value of
tradables to the domestic price numeraire. At the economic discount rate of
12.21 percent (based on the same study), the economic net present value of
the Project is estimated at taka(Tk)7.77 billion, showing economic viability.
Sensitivity test of the economic NPV shows that it becomes negative if the
traffic growth rate goes below 3 percent or if the investment cost overruns
by more than 25 percent.
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APPENDIX 4 Case Illustrations of Distribution
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77
Distribution Analysis
In the Project's case, the difference between financial and economic
flows derives from two factors: (i) some project inputs and outputs having
conversion factors different from unity; and (ii) the Project generates economic
benefits that are not captured as financial benefits. Net stream of both financial
and economic flows are discounted at the economic discount rate of 12.21
percent. The analysis of allocation of the difference of economic from financial
NPV is presented in Table A4.3. Light vehicle owners, bus passengers, truckers/
shippers, power company, and the locality gain from the Project, while the
Government/aid agencies and ferry operators lose.
Light vehicle owners and bus passengers would gain Tk627 million
and Tk1,951 million, respectively. Truckers/shippers would be the largest gainer
and realize savings of about Tk31,094 million. The bridge crossing site and
its surroundings are estimated to gain Tk457 million of environmental benefits
from the increased net incomes from enhanced agricultural production and
the prevention of embankment erosion. The Government/aid agencies would
lose about Tk27,700 million. This is mainly due to the subsidy on the loan
(Tk19,851 million), the government grant (Tk2,455 million), and the premium
lost on the foreign exchange used to purchase tradable components of the
investment cost of the bridge (Tk5,358 million). The Government also loses
tax and tariff revenues on the imported components of VOC saved because
of the Project. The ferry operators would lose Tk1,840 million as the ferry
services are replaced by the bridge crossing.
Poverty Impact
These adjustments allow us to identify the direct income effects created
by the bridge project. By far the most important are the gain to bridge users,
which is predominantly in the form of freight traffic, and the loss to the
Government which meets the cost of the project (and gains the toll revenue).
This is equivalent to the distribution analysis of Appendix 25 of the ADB
Guidelines (1997). Going beyond this to estimate both the indirect income
effects and the project poverty impact requires further information.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
The indirect income effects of the Project arise from the fact that income
to both the freight sector and the Government may ultimately be used in a
variety of ways. In terms of savings in trucking costs at one extreme these
may all stay within the sector as higher profits and at the other they may be
all passed on to end users as lower prices. In practice, a combination of these
two outcomes is likely, with the share going to consumers varying directly
with the degree of competition in the sector. The issue then becomes one of
how many consumers affected by the Project are poor and whether higher
freight profits are used in a way that raises the incomes of the poor. Tracing
particular income effects too far back in the productive chain is likely to be
fraught with difficulty and it is best to avoid linking multiplier effects via higher
expenditure with any individual project on the grounds that if it is simply
higher expenditure that causes higher incomes then such effects can be
obtained from any project, not one in particular. The issue of how additional
government income affects the poor is discussed in the main text.
Where additional or generated traffic is involved, as in this project,
economic value is approximated conventionally by half unit VOC savings. This
will be valid in a competitive market structure where willingness to pay for
transport services (as proxied by half VOC) equals the net economic gain
from using those services. For generated traffic therefore consumer surplus
(the excess of willingness to pay over actual payment) will be divided between
producers, whose output is stimulated by the project and the freight sector,
depending upon the pricing structure of the latter. Again the more competitive
is the sector the higher is the proportion of consumer surplus that will accrue
to producers. Tracing through these effects will require data on both production
activity stimulated by the bridge and on the pricing strategy in the freight
sector.
As an alternative however one can adopt a set of simplifying assumptions
in order to get a first check on how economic returns relate to poverty impact.
These are
(i) that the transport sector is competitive so that ultimately lower
freight costs are passed on in lower end-user prices. The gain to
the poor from lower VOC on normal and diverted traffic could be
allocated in proportion to their share in total income. If the project
involved is an expressway then figures can be national rather
than local;
(ii) that consumer surplus for generated traffic accrues solely to
producers (as consumers of road services) whose production is
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1 Introduction
APPENDIX 4 Case Illustrations of Distribution
Analysis
79
stimulated by the fall in VOC created by the bridge, due to the
competitive nature of the freight sector. If agricultural goods are
involved these gains can be allocated to the poor in proportion
to their share in land in the area of influence of the bridge. If
nonagricultural activity is involved the share of the poor could be
proportional to their share in nonagricultural employment in the
area of influence; and
(iii) that only the poor are employed in the construction of the bridge
and that they gain the difference between the wage paid and
their economic wage or opportunity cost.
The aim would be to use the basic data required for an economic
appraisal and adjust this to obtain an approximate poverty impact. The final
results could be refined if better data could be collected. The latest examples
of poverty impact analysis applied to ADB transport projects include Tajikistan
Road Rehabilitation Project (Case 5 in Appendix 6). As the above example
was taken from a study prior to the current ADB Guidelines, it uses a discount
rate different from 12 percent. The current ADB practice is to use 12 percent.
4
Untitled-1
Loan 1819-TAJ: Road Rehabilitation, for $20 million, approved on 20 December 2000.
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5/17/02, 10:25 AM
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
89:; !+ < !+ $/ = %1>8(
Item
Financial
PV at 10%
Cost savings and increase in consumer surplus
Light vehicles
Buses
Trucks
Avoided truck waiting time (from 2000)
Toll revenues
Diverted traffic
Light vehicles
105
Buses
826.8
Trucks
1,655.3
Generated Traffic
Light vehicles
42.5
Buses
293.7
Trucks
905.1
Electricity interconnector fees
461.3
Savings on stand-alone
power interconnector
Savings from avoided
costs of ferry system
Ferry purchases
Dredging and maintenance
Environmental benefits
Loan
19,630.6
Government grant
2,489.1
Total Benefits
26,409.4
Investment costs
Main bridge
(9,050.3)
River training works
(9,943.6)
Approach roads
(2,090.9)
Technical assistance
(919.7)
Others
(2,048.7)
Operating and maintenance costs
(1,278.9)
Total Costs
(25,332.1)
Net Benefits
1,077.3
Untitled-1
80
Financial PV
at 12.21%
Economic
PV at 12.21%
606.2
1,904.7
27,181.1
3,157.3
74.7
588.3
1,177.8
29.4
203.6
627.4
344.1
Economic
minus
Financial
606.2
1,904.7
27,181.1
3,157.3
(74.7)
(588.3)
(1,177.8)
29.4
203.6
627.4
2,888.3
327.4
460.1
456.9
0
0
0
(344.1)
2,888.3
19,851.2
2,455.4
25,351.9
37,842.4
327.4
460.1
456.9
(1,985.1)
(2,455.4)
12,490.5
(8,898.5)
(9,794.3)
(2,056.9)
(901.2)
(2,041.2)
(1,017.2)
(24,709.3)
642.6
(11,178.7)
(12,396.1)
(2,418.3)
(1,016.2)
(2,041.2)
(1,017.2)
(30,067.7)
7,774.7
(2,280.2)
(2,601.8)
(361.4)
(115.0)
0
0
(5,358.4)
7,132.1
5/17/02, 10:25 AM
onomic
inus
ancial
1 Introduction
APPENDIX 4 Case Illustrations of Distribution
Analysis
Light Vehicle
Passengers
06.2
04.7
81.1
57.3
Bus
Passengers
Truckers/
Shippers
Power
Company
627
Government
Locality
Ferry
Operators
(20.8)
(46.9)
(755.7)
1,951.6
27,936.8
3,157.3
4.7)
8.3)
7.8)
(74.7)
(588.3)
(1,177.8)
0
0
0
4.1)
88.3
0
0
0
(344.1)
2,888.3
27.4
60.1
56.9
5.1)
5.4)
90.5
327.4
460.1
456.9
627
0.2)
1.8)
1.4)
5.0)
0
0
8.4)
32.1
0
627
Untitled-1
1,951.6
0
1,951.6
81
31,094.1
0
31,094.1
2,544.2
(1,9851)
(2,455.4)
(2 , 2 3 4 3 )
456.9
-1,840.8
0
2,544.2
(2,280.2)
(2,601.8)
(361.4)
(115.0)
0
0
(5,358.4)
(27,701.0)
0
456.9
0
(1,840.8)
5/17/02, 10:25 AM
81
82
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5/17/02, 10:25 AM
APPENDIX
5
Incorporating Project Financing in
Distribution and Poverty Impact Analysis
To clarify the link between financial analysis of a project and distribution
estimates, this Appendix extends the project example from Appendix 25 of
the ADB Guidelines (1997) by incorporating the effects of project financing
and profits taxation. Both of these aspects were omitted for simplicity in the
original, but this simplification may have caused some misinterpretation of
what is required.
Before presenting the revised example it is worth restating the different
financial calculations that can be carried out for a project. Three of the most
important alternatives are
(i) Return to total capital: This is the NPV/IRR for all project resources
valued at financial prices, but before any financing arrangements
are incorporated in the analysis. This is the basic financial return
regardless of how the project is financed and before any profits
tax is paid. As a test of viability the FIRR to total capital should
be compared with the weighted average cost of capital discount
rate.
(ii) Return to total capital net of tax: This is the NPV/IRR for all project
resources valued at financial prices, before any financing
arrangements but after profits tax has been deducted. Profits tax
will only be estimated approximately at this stage if interest
payments, which are tax deductible, are not known accurately.
As a test of viability the FIRR to total capital net of tax should be
compared with the weighted average cost of capital discount rate
net of tax.
(iii) Return to equity: This is the NPV/IRR of the project resource flow
going to the project owners (i.e., those with an equity stake). Here
loans are benefits to equity holders when they are received and
costs when the interest and principal are repaid. Returns to equity
must also be after deduction of profits tax. As a test of viability
the FIRR to equity should be compared with the opportunity cost
of investors' own funds.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
From the viewpoint of distribution analysis financial calculations should
be carried out as returns to equity since this allows a disaggregation of financial
returns into: returns to equity, lenders and government (via profits tax). The
returns to these groups are defined as follows.
(i) Return to equity is the discounted value of profits (either retained
or distributed) minus the discounted value of equity investment.
(ii) Return to lenders is the discounted value of loan repayments
(interest plus principal) minus the discounted value of the loan.
(iii) Return to government is the discounted value of the profits tax
payments.
These adjustments can be illustrated taking the telecommunications
project example from Appendix 25 of the Guidelines. The numbers there are
changed in three ways here for purposes of illustration. First, we assume a
loan of 200, which is 50 percent of the investment cost of 400, so that equity
investment is also 200. This loan is to be repaid over 5 years at an 8 percent
real interest rate. Second, to show the role of profits tax in the calculation we
assume an effective tax rate of 20 percent (This means that after allowing for
various deductions and allowances of the Taxable Profits, 20 percent is
deducted as tax). Third, to allow enough Taxable Profit we increase revenue
to 850.
Table A5.1 gives the full financial and distribution calculation and Table
A5.2 gives the supplementary loan calculation. The return to total capital is
50 (Revenue minus Investment and Operating Costs), while the return to equity
is 84.68. Investors gain at the expense of lenders since the loan interest rate
is below the assumed financial discount rate of 12 percent, which implies an
interest rate subsidy. The government gains in the financing calculations from
the payment of profits tax. Whether the distinction between investors and
lenders is critical for distribution analysis partly depends on whether they
belong to different groups. If both are part of the public sector then their
incomes can be included under the general group of government. However
one may be in the public and the other in the private sector or one may be
national and the other may be foreign.
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5/17/02, 10:25 AM
Untitled-1
85
46
to government
to lenders
to total capital
Effective tax rate (%)
84.68
3.36
(38.04)
50
20
(400)
(100)
(100)
(200)
(33.22)
(128.74)
(3.36)
(965.32)
850
200
0
1,050
5
Revenue
Loan
Consumer surplus
Inflow
$
Equipment
Installation
Labor
Others
Interest
Principal
Profits tax
Outflow
FNPV
Item
1.3
1
0.9
1
0
0
0
1
0
CF
" !
190
(520)
(100)
(90)
(200)
0
0
0
(910)
850
0
250
1,100
(120)
0
10
0
33.22
128.74
3.36
0
(200)
250
84.68
(116.64)
3.36
(120)
10
10
250
250
34
ENPV (ENPV-FNPV) Corporation Gov't. Labor Consumers
?9%; (38.04)
33.22
128.74
(200)
Lenders
APPENDIX 5 Incorporating Project Financing in Distribution and Poverty
Analysis 85
85
1 Impact
Introduction
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A5.2
, 2
Balance outstanding
Principal
Interest
)
PV (interest)
PV (principal)
)
Subsidy
+
200.0
0.0
0.0
++
160.0
40.0
16.0
"+
120.0
40.0
12.8
*
80.0
40.0
9.6
7"
40.0
40.0
6.4
"
0.0
40.0
3.2
33.22
128.74
"7"
38.04
.
Loan
Repayment period
Real interest rate
Discount rate
200
5 years
8%
12%
Other aspects of the illustration remain unchanged. The distribution of
differences between economic and financial values creates a similar pattern
of income change as before. Consumers gain the consumer surplus, labor
gains the difference between the financial and the economic wages, and the
government loses the premium on foreign exchange from the purchase of
imported equipment. The final set of income changes is summarized below.
Corporation
Government
Labor
Consumers
Lenders
ENPV
84.68
(116.64)
10.00
250.00
(38.04)
190.00
The important general point is that under some financing arrangements
one would expect the income flows created by financing of a project to be
just as important, if not more so, than those identified from the existence of
divergences between economic and financial prices for the inputs and outputs
of a project. Omission of those flows as a result of basing the analysis on a
return to total capital (before or after tax) can be misleading.
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1 Impact
Introduction
APPENDIX 5 Incorporating Project Financing in Distribution and Poverty
Analysis 87
87
Extension to Poverty Impact Analysis
A question arises as to how losses (or gains) to Lenders explained above
can be linked with poverty impact analysis. Several possible scenarios can
be identified:
(i) Where the Lender is a private financial institution:
Here in principle the proportion of lenders who are poor can be
identified and this ratio can be applied to their change in income.
However, losses (or gains) to the Lender are unlikely to affect the
poor since income changes for a private financial institution will
either impact on shareholder profits or deposit or borrower interest
rates. The poor will not be investors in private financial institutions
and will rarely be affected by commercial bank interest rates.
Further, it would be rare for a private financial institution to
continue for long with a policy of subsidized credit, which is the
implication of an income loss to the Lender. Hence this scenario
is unlikely to be of relevance in poverty impact analysis.
(ii) Where the Lender is a public financial institution:
This scenario is more relevant but can be dealt with relatively
easily, for example, by incorporating the Lender as part of the
Government. In the example in Table A5.1, where the loan comes
from the government the Lenders column can be merged with
the Government column so that the value of the Loan of 200 is
entered with a negative sign and the value of Principal and Interest
payments of 33.22 and 128.74, respectively, are entered with
positive signs in the Government column. The net total for the
Government column is thus -154.68, which is the sum of the original
losses to Government (-116.64) and Lenders (-38.04). The relevant
poverty ratio for government income can be applied to this figure.
In this example if the government had lent to the project at a
commercial interest rate, broadly speaking, the loss to the Lender
would have been zero, but the return to the corporation would
be correspondingly lower at 46.64 (84.68-38.04). In the case of
lending by aid agencies such as ADB, for practical purposes, we
can assume that the external funds are channeled from the
borrower government to a local public financial institution (or a
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
private financial institution acting as a nonprofit agent for the
borrower government). But this refinement does not affect the
basic structure of the analysis (see Case 5 in Appendix 6 for an
illustration).
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1
APPENDIX
Introduction
89
6
Case Illustrations of Poverty Impact Analysis
The following cases extend distribution analysis to incorporate a poverty
focus. All are based on actual reports and recommendations to the President
(RRPs) to which additional work has been added. Case 1 illustrates the point
that economic calculations conducted at world price numeraire can be used
as the basis for distribution analysis, although there is an added complication
not present when the domestic price numeraire is used for economic values.
The poverty impact results are speculative in the absence of knowledge of
how much of the consumer benefits from the project will accrue to poor
consumers. A detailed regional survey would be required to address this
point accurately and the 25 percent figure used is only an approximation.
Case 2 is of an irrigation project from Viet Nam. The original work on this RRP
was supplemented by further EDRC analysis in the field, which allowed a
relatively detailed assessment of the project's poverty impact. The case also
illustrates an important limitation of the poverty impact ratio, which is not an
adequate means of selecting alternatives from a set of projects. In this case
the project with the largest net gain to the poor is the one with the lowest
PIR. Case 3 on a coastal resource management project from Sri Lanka does
not contain a distributional and poverty impact analysis, but some indicative
comments on how one might be carried out. It is included here to draw
attention to a particularly detailed social impact assessment, which unusually
provides detailed data on the incomes of the stakeholders involved. Case 4
on a health sector project illustrates a case in which project benefits are not
monetized and cost-effectiveness analysis needs to be extended to poverty
impact analysis using a second-best headcount approach. This is an area in
which an increasing research effort can enhance the understanding of the
magnitude of the differential impact of health intervention on the poor. Case
5 on a road rehabilitation project is the most recent example in this Handbook.
The analysis was prepared under a separately commissioned study with
specific terms of reference to carry out distribution and poverty impact analysis.
A good aspect of this analysis was the importance attached to the
competitiveness of road transport services as a determinant of the project's
poverty impact.
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It should be noted that the purpose of the illustrations here is to
demonstrate that there have been actual cases of application to ADB projects
and they do not necessarily represent best practices. Project analysts are
encouraged to explore improvement on the practice, for example, by
considering the data collection and analysis suggested in Appendix 3 and
incorporating the project-financing aspect discussed in Appendix 5.
Case 1– Philippines: Transmission Interconnection
and Reinforcement Project
The following is taken from the RRP. 1 The project design is complex
with four components but the procedure and assumptions of economic
analysis are presented well. A reasonable attempt has been made to extend
it to distribution and poverty impact analysis. The assumptions and numbers
are slightly modified from the original. For the illustrative purpose of this
appendix, sensitivity and risk analyses are omitted.
Project Description
Mindanao is one of the poorer regions in the Philippines. It contains
23.2 percent of the country's population. The poverty incidence of the region
is 50 percent as compared with a national average of 36.7 percent. As many
as 62.3 percent of the poor in Mindanao have no access to electricity. The
Project provides high-priority transmission facilities that are required to
evacuate power from Luzon to Mindanao which is otherwise expected to
face power shortages, and that are essential for the operation of the competitive
electricity market. The Project consists of about 416 km of high voltage direct
current transmission lines. Component A (Leyte-Mindanao Interconnection)
provides 23 km of 350 kV HVDC submarine cable, cable terminal stations,
converter stations, 25 km of electrode lines, 28 km of 138 kV alternating current
(AC) transmission lines, and associated substation expansions linking the
1
Untitled-1
Draft RRP-PHI: Transmission Interconnection and Reinforcement Project, for the proposed amount
of $100 million, as of May 2000.
90
5/17/02, 10:26 AM
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
91
geothermal fields in Leyte to Mindanao. Component B (Luzon Transmission
Upgrading) provides about 117 km of 230 kV AC double circuit transmission
lines between San Manuel, Pangasinan, and Mexico, Pampanga, and 15 km
of 230 kV AC double circuit transmission lines from Biñan, Laguna to
Dasmariñas, Cavite. Component C (Mindanao Substation Expansion) installs
additional power transformers and circuit breakers at the existing Bislig,
Butuan, Davao, Kibawe, Klinan, Sta. Clara, and Tindalo substations in
Mindanao. Component D (Power Trading and Pooling System) develops the
power trading and pooling system software and provides required computer
hardware to control and process transactions and functions associated with
the competitive electricity market. Of the total project cost of $600 million,
85 percent is allocated to Component A, 9 percent to Component B, 3 percent
to Component C, and 3 percent to Component D. It is to be cofinanced by
ADB (16.7 percent), Japan Bank for International Cooperation (16.7 percent),
commercial banks (55 percent), and the Government (11.7 percent).
Least-Cost Analysis
Using the EGEAS program (developed by the Electric Power Research
Institute of USA), the National Power Corporation (NPC) performed a leastcost analysis for the Mindanao grid as part of the preparatory work for the
1999 Power Development Program. The first alternative (the Project) allows
for transfer of power generated at independent power producer (IPP) power
plants in Leyte and Luzon to the growing load centers in Mindanao. Surplus
power in Luzon is planned to be available for export to Leyte from 2004 to
2013, peaking at 2,938 GWh in 2009. The weighted average cost of power
from the Leyte and Luzon IPP power plants would range between =
P 2.75/
kWh and =3.26/kWh.
P
The second alternative would be to accelerate the
expansion of the generating capacity in Mindanao using a mixture of generating
plants such as coal, diesel, and hydropower. The equalizing discount rate
(EDR) analysis supports the Project over the second alternative. Using constant
1999 prices and an SCF of 0.83 (this number is outdated and needs to be
updated for future ADB projects in the Philippines), it was shown that both
investment costs and system operation and maintenance (O&M) costs are
lower for the Project than the alternative for discount rates up to 18.5 percent.
The production costs of the Project are lower because the interconnection
allows more efficient use of resources by utilizing power from two IPPs
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
operating the Leyte Geothermal Plants and the planned Iligan power
generating plant in Luzon. NPC's contract with these IPPs is on a take-orpay basis. The interconnection also allows saving in fuel at thermal power
plants in Mindanao.
Financial and Economic Analysis
Approach — Financial and economic analyses are first carried out
separately for Components A, B, and C, respectively, as there is no revenue
and benefits quantifiable separately for Component D. Then the analysis for
the overall Project was done by adding the cost of Component D.
The financial analysis for Component A was based on (i) a project life
of 25 years (after project completion); (ii) an estimated total financial cost of
=16,259
P
million covering two converter stations, HVDC overhead transmission
lines, submarine cable and electrode stations, and strengthening of the
associated AC components; (iii) annual O&M cost of 1 percent of capital costs;
(iv) annual sales ranging between 1,837 GWh and 3,284 GWh; (v) transmission
losses of 1 percent in Leyte, 1 percent over the interconnection and 4 percent
in Mindanao; (vi) cost of purchasing power in Leyte; and (vii) an average
tariff of =2.36/kWh.
P
It is assumed that when the competitive markets are
introduced, the tariff throughout the NPC grid will tend to even out. The tariff
in Luzon would be reduced over time, while the tariff in Mindanao would
increase. To allow for additional benefits from competitive markets through
efficiency gains and improved operations in future, the adopted tariff was set
at 90 percent of the weighted average tariff proposed in March 1999 (P
=2.96/
kWh in Luzon, =2.65/kWh
P
in Visayas, and =1.86/kWh
P
in Mindanao, with a
weighted average of =2.62/kWh).
P
Incremental revenue starts to flow in only
from 2008 when the interconnection would meet additional demand in the
Mindanao system. Before that, the interconnection would displace revenues
from the existing power plants. The interconnection will lead to resource
savings in Leyte, Luzon, and Mindanao. Resource savings in Leyte represent
those costs that would otherwise have to be paid by NPC to IPPs in Leyte and
Luzon for not meeting their take-or-pay contractual obligations. Resource
savings in Mindanao result from fuel savings due to deferred construction of
new plants—backing down diesel-fuelled IPP generating plants totaling 258
MW. The FIRR of this component is estimated at 9.7 percent, which is higher
than the estimated weighted average cost of capital (WACC) of 5.2 percent
and is satisfactory.
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APPENDIX 6 Case Illustrations of Poverty Impact Analysis
93
In the economic analysis for Component A, nonincremental power was
valued on average at =4.43/kWh
P
net of distribution losses and costs. The use
willingness-to-pay (WTP) was based on the present-day cost of operating
private generator sets, which were purchased by affluent households,
commercial establishments and industries to provide power during the 1992/
93 power crisis. Given the higher cost of self-generation, it was assumed that
residential, commercial, and industrial users would only self-generate 27/
percent, 35 percent, and 22 percent, respectively, of their normal power
consumption. The WTP is also based on the present-day cost of using kerosene
lamps for lighting among the poorer consumers in rural areas. Incremental
power demand will mainly come from the connection of rural consumers.
The benefits are credited to the Project from 2008 onwards when there is no
more surplus capacity in the system from the generating plants operational
in 2003. The EIRR for this component is estimated at 17 percent and is
satisfactory as it exceeds the benchmark 12 percent for economic viability.
The financial analysis for Component B was based on (i) a project life
of 25 years, (ii) average cost of power in the northern Luzon Grid, (iii) fuel
costs, (iv) annual O&M cost of 2.5 percent of the capital costs of the transmission
facilities, (v) transmission losses of 3 percent, and (vii) an average tariff of
=2.36/kWh.
P
The alignment of the transmission line follows an existing rightof-way and thus significant cost savings ensue. The FIRR is estimated at 11.4
percent. The economic analysis was based on the average benefit of =4.42/
P
kWh, net of distribution losses and costs. All economic benefits are considered
nonincremental. The EIRR is estimated at 15.4 percent. Component B is
considered satisfactory in terms of both financial and economic viability.
The financial analysis for Component C was based on (i) a project life
of 25 years, (ii) annual O&M cost of 2.5 percent of the capital costs of the
transmission facilities, (iii) system losses of 4.5 percent, and (iv) an average
transmission tariff of =0.27/kWh.
P
The FIRR is estimated at 21.9 percent. The
economic analysis was based on an average benefit of =
P 4.43/kWh net of
distribution losses and costs. The EIRR is estimated at 27.2 percent, indicating
both financial and economic viability of Component C.
The FIRR for the overall Project was calculated by putting together the
above three components and adding the cost of Component D. The FIRR and
EIRR are estimated at 10.2 percent and 16.9 percent, respectively, indicating
both financial and economic viability of the overall Project.
Untitled-1
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Distribution Analysis
For the purpose of distribution analysis, the discount rate of 12 percent
is used in both flows. The original project economic analysis is at world prices,
so that tradables are valued at border prices at the prevailing exchange rate.
Nontradables are valued at economic prices, using a mix of SCF and specific
conversion factors. The SWRF for unskilled labor is less than one, which is
multiplied with SCF to obtain a conversion factor for unskilled labor at the
world price level. As discussed in Fujimura and Weiss (2000), the use of the
world price numeraire (but in domestic currency units) as in this case can
be applied in distribution analysis, provided both financial flows and economic
flows are expressed in the same numeraire. If we use economic prices in the
world price numeraire, financial flows must be adjusted to the same numeraire
by multiplying by the SCF. If we use the domestic price numeraire, economic
flows must be adjusted by dividing by the SCF. Table A6.1 summarizes the
calculations in both numeraires. Although we illustrate the use of the world
price numeraire to make the point of its equal applicability, it would have
been simpler to use the domestic price numeraire for both financial and
economic flows. See also Curry and Weiss (2000, pp.285–289) for more subtle
discussion on the numeraire issue in distribution analysis.
Table A6.1 shows the distribution of the project effects (economic minus
financial present values) among the stakeholders: the Government/the rest
of the economy, consumers of the Project electricity, and labor involved in the
Project. As a result of the Project, consumers and labor gain while NPC and
the Government lose (the original analysis mistakenly indicated that
Government gains). Since NPC is a state-owned enterprise, the loss to the
NPC can be considered the Government’s financial losses from investing in
the Project. The Government’s economic loss is due to inefficient resource
use associated with domestic price distortion relative to world prices (SERF
effects) and constitutes effective net subsidy to the Project. Consumers gain
a consumer surplus, mainly in terms of the difference between the withoutProject cost of electricity and the with-Project expenditure on electricity
and the value of electricity consumed but not paid for. It is assumed that
10 percent of the capital and O&M costs over the project life would come
from the employment of unskilled labor. This labor gains a net economic
benefit because the Project pays wages in excess of the economic
opportunity cost of unskilled labor.
Untitled-1
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APPENDIX 6 Case Illustrations of Poverty Impact Analysis
95
Poverty Impact Analysis
For the purpose of poverty impact analysis, net economic benefits
accruing to each stakeholder must be allocated between the poor and nonpoor.
(i) Along the line explained in the main text, the marginal government
expenditure required for project cost finance that accrues to the
poor is approximated by the income share of the poor in GDP,
which is 14 percent. This ratio is applied to both NPC and the
Government.
(ii) The existing access to electricity is rather biased in favor of the
nonpoor. In Mindanao, 38 percent of the poor has access to
electricity while 82 percent of the nonpoor does. Therefore, the
principal channel of distribution of consumer surplus attributable
to the Project reaching the poor will be the incremental supply
of electricity, particularly due to rural electrification. The poor
will benefit from lower electricity costs from the transmission
interconnection and reinforcement under the Project, compared
with the higher cost of thermal energy. Based on the projected
share of poor consumers, their electricity consumption pattern
and the number of the poor living below the poverty line, it is
assumed that 25 percent of the consumer surplus goes to the poor.
(iii) Based on the projected rural labor employed by the Project, it is
assumed that 20 percent of the economic benefits accruing to
the unskilled labor goes to the poor living below the official poverty
line.
(iv) Based on the above assumptions, the Project benefits going to
the poor below the poverty line are estimated at =2,846
P
million
(in domestic price numeraire), or the poverty impact ratio is 34
percent, as shown in Table A6.1. In addition to the quantified
benefits, there are nonquantifiable economic and social benefits
to the poor in the form of enhanced opportunity of human capital
development induced by the efficient power supply. The analysis
here indicates that a significant portion of the Project benefits
goes to the poor, perhaps significantly exceeding the current poor's
share in national income.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A6.1
Table A6.1
; ;
8
9 ,
&P
=
P (
:
% '
Benefits
Capital Costs
Operating Costs
, %
SCF
SERF
57,067
(13,493)
(45,095)
(1,521)
0.83
1.204819
:
% 9 47,366
(11,199)
(37,429)
(1,262)
4
% '
59,597
(12,230)
(40,363)
7,004
4
:
12,231
(1,031)
(2,934)
8,266
Gains and Losses
Proportion of the Poor (%)
Benefits for the Poor
Poverty Impact Ratio (%)
8
,
(=
P million)
Financial
Present
Value at 12%
Benefits
Capital Costs
Operating Costs
, %
57,067
(13,493)
(45,095)
(1,521)
Financial
Present
Value at 12%
59,597
(12,230)
(40,363)
7,004
Economic
Present Value
at Domestic prices
71,804
(14,735)
(48,630)
8,439
Gains and Losses
Proportion of the Poor (%)
Benefits for the Poor
Poverty Impact Ratio (%)
Untitled-1
96
5/17/02, 10:26 AM
Economic
minus
Financial
14,737
(1,242)
(3,535)
9,960
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
Table A6.1
, $
3 4
;
/
$
<
4
12,231
(1,157)
(3,407)
126
473
Total
(1,262)
14
(177)
(4,564)
14
(639)
12,231
25
3,058
600
20
120
7,004
2,362
34
NPC
Government/
Economy
Distribution of Project Effects
Consumers
Labor
14,737
152
570
(1,394)
(4,105)
Total
(1,521)
14
(213)
(5,499)
14
(770)
14,737
25
3,684
722
20
144
34
Untitled-1
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5/17/02, 10:26 AM
8,439
2,846
97
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Case 2 – Viet Nam: Second Red River Basin Water
Resource Sector Project
The following is based on the RRP 2 and the associated EDRC staff
work that contributed to the related supplementary appendix in the RRP.
The original economic analysis was limited to the estimation of EIRRs for
subprojects, but in view of the importance of distributional and poverty
impacts associated with the Project, EDRC staff provided additional modest
resources to augment the analysis.
Project Description
The Red River basin is home to about one third of Viet Nam's population.
Poverty incidence in the upland provinces of the basin is 59 percent,
considerably higher than the national average of 37 per cent (using World
Bank poverty line). To tackle the poverty situation in the basin area, the Project
adopts a multidimensional approach through better management of the most
important resources—water, and investment in physical infrastructures such
as dams, canals, and extension services. The Project has two components.
Component A addresses integrated water resources management and
associated institutional building. These will include capacity building, public
awareness and education programs for water resources management, pilot
testing in water licensing and wastewater discharge permit system, water
quality monitoring network, and flood forecasting and warning system.
Component B provides infrastructure improvements and the necessary
agricultural support services identified by communities. It will comprise a
number of subcomponents, the most important ones are (i) subprojects to
improve irrigation systems and watershed protection in the uplands; and
(ii) subprojects to improve delta irrigation and drainage systems. Of the
total project cost of $137 million, $22 million is allocated to Component A
and $115 million to Component B. Under the investment Component B the
Project Implementation Unit (PIU), in collaboration with local authorities
and organizations, will identify subprojects for possible rehabilitation and
upgrading.
2
Untitled-1
Draft RRP-VIE: Proposed Second Red River Basin Water Resource Sector Project, May 2000.
98
5/17/02, 10:26 AM
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
99
Economic Analysis
The economic analysis was conducted for three representative
subprojects, two in upland areas (Yen Binh and Ngia Lo districts in Yen Bai
Province, 200 km northwest of Hanoi city) and one in the delta (Gia Thuan
district in Bac Ninh Province, just outside Hanoi city) to examine the economic
viability of indicative subprojects under the Project, which is expected to
support more than 30 subprojects for rehabilitation and upgrading.
In Yen Bai province, several irrigation schemes were found to be
experiencing rapid sedimentation and will require repair and upgrading of
irrigation infrastructure such as the headworks and related delivery facilities.
The Project investment will comprise rehabilitation of weirs, lining of main
canals, and construction of secondary canals of selected irrigation schemes.
In Bac Ninh Province, the irrigation schemes are suffering from aging (30
years old) and are in need of repair, and experience water shortages in winter
and spring and flooding in late summer. Rehabilitation and improvement of
the irrigation and drainage infrastructure in the Gia Thuan subproject will
mainly involve installation of new pump facilities and upgrading of irrigation
canal-related structures.
Increased production in all three irrigation subprojects is projected to
be derived from increased crop yields, higher cropping intensity as well as
additional agricultural land, which were mainly rainfed areas prior to Project
implementation, converted to irrigated agriculture. It is expected that at full
development (in Year 6 of Project implementation) all rainfed areas in the
three subproject areas will be fully irrigated. Under Yen Binh and Ngia Lo
subprojects, 333 ha and 206 ha, respectively, of irrigated land will be covered
for irrigation upgrading. Under the Gia Thuan subproject, a total of about
17,500 ha of irrigated areas will be covered. As a result of converting presently
rainfed rice and nonrice areas into irrigated diversified agricultural lands,
the cropping intensities in the three subproject areas are expected to increase
from 159, 151, and 202 percent under without-Project situation, to 242, 232
and 232 percent under with-Project situation, respectively. Paddy rice yields
in the subproject areas are expected to increase from current levels of 3.64
mt/ha, 3.48 mt/ha, and 5.0 mt/ha, respectively, for spring irrigated rice to 5.2
mt/ha. Yields of maize, sweet potato, and vegetable crops likewise are expected
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
to increase as a result of improved water availability and delivery. At full
development in 2006, an aggregate net incremental annual production of
2,200 mt, 1,300 mt, and 51,200 mt, respectively, is envisaged to be realized
in the three subproject areas.
Economic pricing was done under the domestic price numeraire. All
subprojects are assumed to have a 25-year life. Subproject investment and
recurrent costs were first segregated into their foreign and local cost
components. The foreign exchange component, together with about 20 percent
of local currency costs which were assumed to consist of tradable items, were
shadow priced using a shadow exchange rate factor (SERF) of 1.043 (shadow
exchange rate of dong(D) 14,599/US$ as opposed to official exchange rate
of D14,000/US$). All other local currency costs were expressed in economic
values following their financial values. Shadow wage rate factor (SWRF) of
0.85 was applied to both on-farm labor and the labor components of the
investment costs. Economic prices of rice and maize as well as agricultural
inputs such as urea, triple superphosphate (TSP) and potassium chloride (KCI)
were adapted from actual average international prices of traded commodities
presented in the World Bank Global Commodity Markets (April 1999). In arriving
at farmgate prices, border prices for international freight and handling charges
were added and local costs for transportation, bagging, handling, and
processing were added. Economic prices of sweet potato and vegetables
assumed to be grown in the subproject area were mainly based on their
farmgate financial prices, adjusted for transportation costs. Based on the
estimated incremental economic costs and benefits, the net economic
benefits attributed to agricultural crop production were calculated which,
in turn, served as the basis for estimating the EIRRs of the three subprojects.
The base case EIRR value was calculated to be 20.6 percent for the Yen Binh
subproject, 12.4 percent for the Ngia subproject, and 25.1 percent for the
Gia Thuan subproject. This implies that lowland irrigation has a relatively
better economic return than upland irrigation—a sign of locational trade-off
between efficiency result of the subproject and targeting of high poverty
incidence areas. Sensitivity analysis is omitted here.
Untitled-1
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APPENDIX 6 Case Illustrations of Poverty Impact Analysis
101
Distribution Analysis
As the three subprojects do not envisage revenue-generating project
entities, the original analysis did not carry out financial analysis for the
subprojects as a whole, which would have gone further than farm budget
analysis. Here financial prices and financial NPVs corresponding to economic
ones have been matched one to one in order to examine the distributional
aspects of the subprojects’ impact. Also an extension to poverty impact analysis
has been attempted. To carry out these analyses, it was necessary to collect
both primary and secondary data on transfer payments related to project
transactions and poverty incidence in the subproject areas. The following
major groups of data were collected:
(i) project-specific economic and financial data: those on prices,
district and provincial market, marketing margins of input
suppliers and processors, subsidy, taxes, local wage and
unemployment rates for each of the three subproject sites; and
(ii) data on commune-level poverty incidence in each of the three
subproject sites as well as national-level poverty incidence.
For distribution analysis, three main stakeholders were identified: (i)
farmers, (ii) labor, and (iii) the Government (or the rest of the economy), among
which the projected net benefits were distributed. Benefits accruing to onfarm labor have been combined into farmers’ benefits. The domestic price
numeraire was used in both financial and economic flow calculations and a
discount rate of 12 percent was used to calculate net present values (NPVs)
of both flows. A most outstanding feature of the projects such as these, where
there are no clear revenue-generating (or cost recovery) agents involved, is
that all financial benefits tend to accrue to project beneficiaries (farmers) while
all financial costs accrue to government. One qualification, however, is that
the original PPTA for this Project did not directly identify the farmgate prices
facing farmers but instead derived them backward from border prices, either
due to unavailability of the direct data in the field or time constraint. If, for
example, the farmgate-to-border middlemen costs have been underestimated,
net financial benefits accruing to farmers might be overestimated in the
summary figures presented here. A more accurate assessment and verification
Untitled-1
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102
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
on the farmers' financial impact would require information on farm operations.
Nonetheless, the summary presentation here remains useful for illustrating
the nature of distributional analysis for this category of projects.
Yen Binh Subproject
Table A6.2 suggests that farmers are a significant net gainer while the
Government (rest of the economy) is a significant net loser from this subproject.
Hired labor is a net, though not significant, gainer. The last group's gain is
derived from the divergence between financial on-farm wage actually received
and their economic opportunity cost of labor. Farmers' net gains are derived
from the subproject's net financial gains (net of very modest irrigation fees)
as well as from the divergence between prevailing financial on-farm wage
and opportunity costs of labor. (It is worth noting that while farmers own
labor, it does not enter as financial costs in farm budget analysis—it does
enter as financial cost either fully or partially in the financial analysis as used
to match economic analysis.) A large part of the Government net loss is
attributable to the expenditure to support investment and recurrent costs for
the irrigation upgrading. Once this portion is netted out, the remaining gains
and losses to the Government consist of a number of further transactions.
The Government provides a sizeable amount of subsidies to farmers in the
region through controlled farm inputs prices (seed, fertilizer, pesticides) and
subsidized O&M costs of the irrigation system. However, these losses to the
Government are partially offset by additional tax revenue on purchased materials
for irrigation investment as well as by the positive effect of increased production
of tradable outputs (SER effect). This disaggregated analysis on the
Government gains/losses highlights some important issues. First, one needs
to consider the extent of fiscal impact of supporting the irrigation investment,
and the fiscal sustainability of the recurrent costs of the irrigation system.
This aspect tends to be neglected in the analysis where only economic
analysis is done to justify irrigation projects. Ideally, financial sustainability
analysis should be included in the economic analysis. Although individual
irrigation projects may be marginal to the total public expenditure in the
agriculture sector, accumulation of similar projects could have a significant
bearing on the fiscal affordability. Second, the analysis here clearly
demonstrates a significant distortion as farmers will be heavily subsidized
in their production activities. One would need to interpret and justify this
Untitled-1
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5/17/02, 10:26 AM
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
103
treatment of farmers in relation to arguments such as the peculiar risks involved
in farming such as volatile crop yields and prices and national food security
policy, as well as in relation to poverty reduction objectives. During the field
visits, discussions with government officials and the local authorities suggested
that the current policy of subsidizing farm inputs are likely to remain in the
foreseeable future.
Ngia Lo Subproject
The gains/losses profile between the three stakeholders and the causes
of economic benefits and losses are explained in a similar way. The only
difference from the above subproject is that the Government subsidy to farmers
is in the form of controlled inputs prices only but does not include irrigation
recurrent costs. In the upland provinces, only a fraction of the irrigation
schemes are maintained by the provincial agricultural authority and the rest
are managed by farmers themselves or by farmers' associations. While the
dam and main irrigation canals in Yen Binh district are maintained (and
subsidized) through the Department of Agriculture and Rural Development,
those in Ngia Lo district are self-run by farmers. Compared with the above
subproject, the size of farmers’ gain relative to the Government loss is not as
great (roughly 1 to 1; this ratio for the above subproject is roughly 3 to 2). This
comparison is consistent with the above EIRRs for the two subprojects.
Ghia Thuan Subproject
Again, the basic distribution profile is the same as the above two
subprojects. However, the relative magnitudes of farmers gain and Government
loss is significantly different. The farmers' financial gain is significantly larger
than Government financial loss, making the farmers' economic gains almost
five times as large as the Government economic loss. Also the difference in
the government transfer profile is that the subsidy to the farmers is in the
form of O&M costs of the Irrigation and Drainage Management Company, as
well as the subsidies provided through cheap electricity on irrigation pumps.
However, there is no subsidy in terms of controlled input prices. In the delta
area, farmers rely on pump irrigation that is heavily dependent on subsidy.
Untitled-1
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104
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A6.2
Table A6.2
@ ; @ ; ! ) &
(
:
%
5
/$
4
%
4
:
:=
:
;
/
<
24,5!,>85 ?@4$)
!
5
!
$
- Project net output
- Investment
- O&M
, %
13,628
(9,278)
(312)
14,543
(8,405)
(550)
915
873
(238)
4,038
5,588
1,550
Gains and Losses
;
<
A"+7
Proportion
of the Poor (%)
Proportion of the Poor
(%)
66
Net Benefits for theNet
PoorBenefits for the Poor
9,642
Poverty Impact Ratio
!
Present
ValueCost
of Project Economic Cost
Present Value of Project
Economic
Net Benefits
for the
Poor per Project Cost
,5
-
3$
5,;!<?85 ?@4$)
!
5
- Project net output
!
$
- Investment
- O&M
, %
8,714
(8,651)
(365)
9,243
(8,549)
(321)
529
102
44
(302)
373
675
;
<
7A
Gains and Losses
Proportion of the Poor(%)
39
Proportion of the Poor (%)
Net Benefits for theNet
PoorBenefits for the Poor
3,609
!
"7"
Poverty Impact Ratio
Present Value of Project
Economic
Present
ValueCost
of Project Economic Cost
,5
-
3$
Net Benefits
for the
Poor per Project Cost
13,628
14,609
66
9,642
8,714
9,254
39
3,609
$;>!)>8,85 ?@4$)
!
5
!
$
, %
- Project net output
- Investment
- O&M
132,431
(23,881)
(13,558)
161,656
(23,628)
(18,408)
29,225
253
(4,850)
94,992
119,620
24,628
;
<
A*
Gains and Losses
Proportion of the Poor
(%)
25.5
Proportion
of the Poor (%)
Benefits for the Poor
39,227
Net Benefits for the Poor
!
++
Poverty Impact Ratio
Present Value of Project
Economic
Cost
42,036
Present Value of Project Economic Cost
,5
-
3$
Net Benefits
for the
Poor per Project Cost
Untitled-1
104
5/17/02, 10:26 AM
132,431
153,833
25.5
39,227
2
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
Table A6.2
; ! ) =
/
9
4
981
981
805
158
(1,348)
805
158
(9,179)
*
&7A#7(
66
12
66
12
104
(1.101)
104
(1,101)
1.55
8,954
8,954
0.97
(Thisisisdifferent
different
from
benefit-cost
+7# (This
from
benefit-cost
ratio)ratio)
-562
123
44
540
:
;
/<
1,007
(53)
(275)
540
4
;/4
)1
4
4
(1,073)
121
37
8
1
34
>
9
4
167
-562
954
(9,590)
8,645
8,645
32
550
-53
32
497
(9,016)
"#
&7A+7(
167
(9,049)
39
12
39
12
65
(1,086)
65
6.96
8,871
8,871
+7
from
benefit-cost
ratio)ratio)
0.29 (This
(Thisisisdifferent
different
from
benefit-cost
21402
21,402
121
1,627
(6,477)
1,748
(6,477)
105
Total
372
372
2,588
2,588
808
7,823
-676
808
7,147
(37,439)
A#*
&A7"(
1,748
(35,961)
25.5
12
25.5
12
446
(4,315)
446
(4,315)
0.30
42,036
+* (This
from
benefit-cost
ratio)ratio)
0.84
(Thisisisdifferent
different
from
benefit-cost
Untitled-1
Total
5,588
5,588
Total
119,620
119,620
35,358
35,358
5/17/02, 10:26 AM
105
106
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Poverty Impact Analysis
The data analysis started off by compiling project-specific communelevel data on poverty incidence among farmers. Such detailed data were only
available through the district and province offices of the Ministry of Labor,
Invalids and Social Affairs (MOLISA). The three subproject areas comprise of
4, 4, and 45 communes, respectively, and "hungry and poor households" are
recorded for each commune by MOLISA. These data are aggregated for each
subproject level (Table A6.3). However, these MOLISA data use a very stringent
criterion equivalent to food poverty as they are designed to target government
social programs. It results in a low national poverty incidence of 17 percent.
When the more general poverty line based on food and nonfood basket is
used, the national poverty incidence is 37.4 percent (World Bank provided
the technical inputs and documented them in the Government-Donor-NGO
joint report on Attacking Poverty: Vietnam Development Report, 2000). The
latter data are considered more appropriate for the analysis here but they are
based on sample surveys and do not provide detailed project-specific poverty
incidence data. Therefore, as a compromise, the MOLISA data on poverty
incidence were converted to the numbers consistent with the general poverty
line (labeled WB in Tables A6.3 and A6.4) using a conversion factor based on
regional level poverty ratio between the two sets of data. As all three
subprojects belong to the Northern upland region, the conversion factor for
that region, i.e. 2.63, was used to scale up the project-specific poverty data
obtained from MOLISA offices.
Table A6.3
+ $'(
Subproject
MOLISA
Yen Binh
Ngia Lo
Ghia Thuan
,
Untitled-1
106
24.9
14.9
9.7
Conversion Factor
2.63
2.63
2.63
#
5/17/02, 10:26 AM
WB
65.5
39.2
25.5
#
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
107
Table A6.4
# 6 )! -7, Percentage of Poor and Hungry Households
in Total Population
WB (%) a
Region
Northern Upland
Red River Delta
North Central
Central Coast
Central Highlands
South East
Mekong Delta
,
a
b
59
29
48
35
52
8
37
#
MOLISA (%) b
Ratio
(conversion factor)
22.4
8.4
24.6
17.8
25.6
4.8
15.4
+
2.63
3.45
1.95
1.97
2.03
1.67
2.40
#
The data in this column were obtained from Government-Donor-NGO Joint Report "Attacking Poverty", 1999.
MOLISA data was obtained from national and provincial offices and the HQ of MOLISA.
The poverty incidence data derived using a conversion factor as above
are used as the proportion of the gains/losses of farmers and labor going to
the poor. The poverty incidence among farmers and off-farm laborers are taken
to be the same as they are not significantly different in the project area. It is
plausible that hired laborers will be mobilized from near the project area. For
the parameter of incidence of marginal government expenditure/income
between the poor and nonpoor, 12 percent derived in Appendix 7 can be
applied.
The results of poverty impact analysis are shown in the lower half of
Table A6.2. A comparison of the three subprojects brings out interesting
implications for the Poverty Reduction Strategy in relation to the use of the
poverty impact ratio (PIR) as a guiding indicator. First, the PIR for the Yen
Binh subproject is greater than unity (1.14) and that for the Ngia Lo subproject
is significantly higher than unity (6.96). This extreme result is partly attributable
to the outstanding feature of the subproject (farmers being a significant gainer
and Government being a significant loser) combined with the differential
proportions of the poor applied to these two stakeholders’ gain/loss (66 percent
for farmers and 12 percent for the Government). Also the relative magnitude
of farmers' gain to the Government loss contributed to the extreme result.
The interpretation here is that the two subprojects have an "ultra-pro-poor"
nature and their benefits accruing to the poor are more than significantly
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
greater than their income share in GDP (12 percent), easily qualifying as poverty
interventions. In contrast, the Ghia Thuan subproject has a low PIR (0.30)
mainly due to the smaller proportion of the poor among target lowland farmers
compared to the first two subprojects. Nonetheless, this subproject can be
considered significantly benefiting the poor disproportionately relative to their
income share in GDP (12 percent).
Second, even when all three subprojects satisfy the minimum criterion
of economic viability (EIRR >12 percent) and therefore justified on efficiency
ground, the interpretation as to how to rank the three subprojects on poverty
impact aspect is not straightforward. Despite the extremely high PIR for the
Ngia Lo subproject, its economic NPV going to the poor is only a fraction of
those for the other two subprojects. This illustrates starkly the inadequacy of
only looking at the PIR as a guide for poverty reduction impact. In absolute
terms, the Ghia Thuan subproject, despite its modest PIR, has the highest
economic NPV going to the poor. Therefore, in the context of the sector project
in which the total project fund will be used to finance a series of subprojects,
those similar to the Ghia Thuan subproject can be selected without reservation.
There is a parallel here with the IRR and NPV comparison, where it is well
known that in simple choices the NPV criteria should be preferred. However,
where there is a fixed budget for irrigation investments, one could rank
alternatives on the basis of the ratio of economic NPV going to the poor divided
by the present value of subproject economic costs. This ratio compares the
three subprojects' poverty impact by the same yardstick (but it should not be
confused with a conventional project benefit-cost ratio). The purpose is to
compare the subprojects' efficiency in terms of poverty impact. As shown
in the last row of the tables, net benefit for the poor per subproject cost is
highest for the Yen Binh subproject, followed by the Ghia Thuan subproject.
The low number for this ratio for the Ngia Lo subproject as well as its smallest
total NPV to the poor indicates that this subproject is the most inferior among
the three in terms of both economic efficiency and poverty reduction impact.
The Yen Binh subproject is both satisfactory in its pro-poor nature and
superior to the other two subprojects in terms of efficiency of poverty impact.
While subprojects with a benefit distribution profile like the Ghia Thuan
subproject are initially a preferred choice, those with a benefit distribution
profile similar to the Yen Binh subproject may become the choice at the
margin when the fund gets exhausted.
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APPENDIX 6 Case Illustrations of Poverty Impact Analysis
109
Conclusion
Apart from the above comparative interpretation of poverty impacts
associated with the three subprojects, some general implication can be drawn
from the distribution and poverty impact analysis here. In all three subprojects
farmers are by far the largest beneficiaries of the Project. Hired laborers will
also benefit in all three subprojects. The Government is a significant loser
from all subprojects. Especially farming in the upland and mountainous regions
of the country will be effectively heavily subsidized both through the fiscal
support to the upgrading and maintenance of the irrigation systems and
through various indirect subsidies to the farming activity. This makes the
financial sustainability of upland projects to some extent dependent on the
fiscal affordability and continuation of the current Government's subsidy
policies. Even though there is an apparent political commitment on the part
of the Government to continue supporting poor farmers, there is a need to
make a realistic assessment on its ability to finance such arrangements in the
medium to long term.
Resource implication: As this case study was carried out by EDRC staff
as a pilot exercise after the PPTA had been done without distribution analysis,
the extension of economic analysis to distribution and poverty impact
analysis involved some technical difficulties. Nonetheless, considering the
modest resources spent and that the EDRC staff did it hands-on for the
first time, it is believed the exercise was worth the cost.
Case 3 – Sri Lanka: Coastal Resource Management Project
The following is taken from the RRP 3 and the project preparatory
technical assistance (PPTA) report that formed its basis. The analysis is
unusual in that it contains a rigorous rapid social assessment of households
in the area of project influence. However, in the economic analysis of the
RRP there was no attempt to integrate the social information with the
Project's economic impact.
3
Untitled-1
Loan 1716-SRI: Coastal Resource Management, for $40 million, approved on 7 December 1999.
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Project Description
The Project will cover six districts in the Northwestern, Western and
Southern provinces, targeting about 76,500 households, representing about
75 percent of the total households living in the coastal areas covered by the
six districts. The Project will have four components: (i) coastline stabilization,
which will address the problem of coastal erosion and develop preventive
management schemes; (ii) coastal environmental and resource management,
which will address coastal resource degradation and include activities to
reduce pollution in lagoons; (iii) fisheries resource management and quality
improvement, which will aim at sustainable coastal fisheries management
supported by the construction of harbors/anchorages and ancillary facilities
to improve fish quality and reduce handling losses; and (iv) institutional
strengthening for the Ministry of Fisheries and Aquatic Resources Development
(MFARD) and other agencies and community organizations concerned. The
total project cost of $80 million is to be cofinanced by ADB ($40 million), the
Netherlands ($12.8 million), the Government ($27.2 million) and beneficiaries
($0.1 million).
Economic Analysis
The benefits of the coastal stabilization component include (i) houses
and buildings on the land to be saved; (ii) avoidance of removal and
administration costs, which would have been incurred if the land had eroded;
(iii) avoidance of the interruption of people's work that would have been caused
by the erosion; and (iv) the opportunity cost of land. In addition, without the
Project, coastal erosion will turn away some tourists and reduce their
spending. A conservative estimate is made that both tourist arrivals and
per tourist spending would decrease by 1 percent if the beaches were to
deteriorate as expected without the Project. The economic internal rate of
return (EIRR) for this component is estimated at 17.2 percent. Without the
tourism benefits, the EIRR would be only about 5 percent.
The coastal environment and resource management component is
designed to address the unsustainable and conflicting uses of coastal and
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5/17/02, 10:26 AM
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
111
marine resources in eight selected sites along the southwest coast. The
incremental benefits arise from the difference between resource use with
and without the Project. It is assumed that the unsustainable use without the
Project will lead to an incremental loss of 1 percent per year of the habitat/
resource value. This assumed level is well below the 2.5–3.0 percent rate of
deforestation in Sri Lanka and about equal to the annual loss of coral cover
in the Hikkaduwa area. Only some of the benefits can be quantified and valued
for inclusion in the analysis: e.g., mangrove vegetation-based products and
fisheries products, sustainable recreation uses, and eco-tourism benefits
associated with the preservation of mangrove, wetland, and reef biodiversity.
The overall EIRR for the eight sites is estimated at 20.6 percent.
Under the fisheries management and fish quality improvement
component, two fishery harbors and two anchorages will be constructed in
four sites to serve fisheries boats. This will result in an increase in fish landings,
a higher proportion of multi-day boats, and a greater number of annual trips
each boat will be able to make. In addition, the fisheries management and
fish quality improvement activities are expected to lead to an increase in the
harvest of fish (as a result of the shift from offshore fishing to deep-sea fishing),
reduction of losses during fish handling, transport and marketing, and an
incremental increase in the value of fish. The EIRR for this component is
estimated at 12.1 percent.
For the Project as a whole, 20-year net incremental benefit streams of
the three components and the cost of capacity building component are
combined. The overall EIRR is estimated at 15.3 percent. Regarding the
sensitivity of project economic viability, either an increase in costs or a decrease
in benefits by 20 percent reduces the EIRR to just over the acceptable level
of 12 percent. However, considering the other nonquantifiable benefits such
as institutional strengthening of MFARD, enhanced beneficiary participation
in resource management, establishment of strong partnership among local
government, the communities and NGOs, etc., the Project is considered
justified.
Due to the nature of the project, in which there are no revenuegenerating project entities, financial analysis was not carried out. However,
with more resources devoted to stakeholder identification and the
quantification of benefits, it would have been useful if an attempt had been
made to allocate income changes among the various stakeholders: e.g.,
fishery-related operators, distributors/wholesalers, tourism operators,
unskilled laborers, etc.
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Use of Social Assessment in Beneficiary-Targeted Project Design
The preparation of the Project provides a good example of how a well
structured social assessment helps project design for targeting beneficiaries.
A rapid social assessment (RSA) was carried out within the identified project
area to assist the design of the coastal resource management component.
The RSA of 750 households employed a combination of structured surveys
and stakeholder workshops and focus group discussions (FGDs) among
selected communities within the identified project sites. The results of the
RSA and FGDs were analyzed and, together with other pertinent secondary
information, discussed with representatives of local institutions and selected
coastal communities and districts.
As shown in Table A6.5, of the total population of about 100,000
households in the coastal areas of the six districts, about 24 percent fall below
the poverty threshold. Among the people above the poverty threshold, 13
percent are considered very poor, 22 percent are poor, 17 percent are low
income earners, 13 percent are moderate income earners, and 11 percent are
high income earners. Unfortunately, however, there is no technical
documentation in the original PPTA regarding the statistical inference of
the income poverty measurement except that the households were chosen
randomly. Therefore, it is difficult to tell the reliability of the details of household
income levels presented. The RSA here is best seen as a device to reveal
beneficiary demand qualitatively and to serve project design rather than a
substitute for poverty measurement per se for the project area. Appendix 3
addresses the issue of how the use of existing household survey data could
assist PPTA in a cost-effective way of estimating the proportion of the poor
beneficiaries in the project influence area.
The last two brackets in Table A6.5 include landowners, large vessel
owners, large processors, multi-day boat owners, prawn farm owners, boat
renters, lime kiln operators, and small mechanized boat owners. Coastal
households will be assisted in identifying and managing livelihood activities
that will give them additional income and improve their quality of life. Small
fish processors and small-scale fishers, who rank among the low-income
groups, will be able to gain access to livelihood credit for the purchase of
insulated fish boxes, processing equipment, packaging units, and working
capital. The health and personal well-being of community residents will also
Untitled-1
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Untitled-1
'
113
2,967
45,001 – 63,000
5/17/02, 10:26 AM
of total households
as percentage
Target beneficiaries
incidence (%)
Poverty
below poverty line
Households
households
78
28
3,919
13,800
980
9,001 – 18,000
Total
1,090
18,001 – 30,000
< 9,000
1,849
poverty line
Below
30,001 – 45,000
poverty line
1,877
2,015
63,001 – 80,000
above
1,642
1,380
45
80,001 – 100,000
> 100,000
+6
#+%
0
74
21
6,720
31,697
1,141
2,092
3,487
4,152
6,434
6,149
4,755
3,487
"
6
73
21
1,033
4,919
177
300
556
694
1,107
758
738
590
73
21
4,275
21,058
1,011
1,411
1,853
2,738
4,675
3,959
2,822
2,590
5 75
"
3
4&
78
26
3,458
13,509
689
1,175
1,594
1,486
3,229
2,405
1,675
1,256
6
8&
78
32
4,980
15,759
1,434
1,434
2,112
1,828
3,357
2,143
1,560
1,891
9
'
5,432
7,503
11,451
12,774
21,769
17,428
13,192
11,195
:
76
24
24,385
100,742
Table A6.5
* = 100
6
7
11
13
22
17
13
11
:
'
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
113
114
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
improve with the provision of clean water, latrines, and sanitary drainage
facilities. Community education and social preparation will raise the
awareness of low-income community households.
The RSA revealed that women in the coastal areas are engaged in a
variety of tasks aside from housekeeping and child-rearing, as shown in Table
A6.6. With the increasing need for women to contribute to household income,
they will benefit from the skills training to be provided under the Project.
Women in the coastal communities showed a high level of awareness of shared
problems and in several places, they have unknowingly formed informal
groups composed of relatives and neighbors for income augmentation
purposes. In some areas, women's groups have been purposefully organized
by NGOs for credit assistance, development of income-generating activities,
skills training, and provision of social infrastructure. Women are envisioned
to be the targets of livelihood development activities such as small-scale fish
processing, handicraft making, garment-making, and the like and will be able
to access microcredit for these projects. They are also expected to contribute
significantly to the promotion of programs, practices, and services concerning
health, hygiene, and sanitation. The active and targeted support of the NGOs/
CBOs will assist in educating them and opening their eyes to better prospects.
Combining Social and Economic Analysis
As noted above no financial analysis of the Project is carried out since
with the exception of minor toll charges for use of the harbors, this generates
no income for the project entity. Some economic pricing is done by applying
an SCF and a specific labor CF. However, since the data are only given in
summary form in the RRP and its associated appendixes, it is not possible
to conduct a full distribution analysis following the procedures in Appendix
25 of the ADB Guidelines (1997). Nonetheless it is possible to get a broad
picture of the beneficiaries from a comparison between project economic
costs (which are all borne by the Government) and project economic benefits
(which all go directly to the private sector). These are summarized in Table
A6.7, with present values discounted at 12 percent.
The significant point here is that the Project creates three main sets of
benefits, corresponding to its three separate components, and the share of
the poor will probably be very low in at least two of these. A poverty impact
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APPENDIX 6 Case Illustrations of Poverty Impact Analysis
115
calculation requires estimation of the proportion of the three types of benefit
given here that accrues to the poor.
The RSA uses a poverty line of SLRs30,000 per year (approximately
$1.2 per day) for the very poor and one of SLRs45,000 (approximately $1.8 per
day) as threshold for the poor. In the study areas 24 percent of households
are very poor and 37 percent are poor by these definitions. From the RSA the
main activities of the very poor are as part-time fisherfolk, canoe repairmen,
casual workers and coir fiber/rope makers. For those with an average income
of SLRs30,000–45,000 the main activities are listed as net repairs, fish selling,
small agriculture, and coral collecting. Although the fishing community is
targeted as a major beneficiary both fishing crews and lagoon fishers are
shown as above the poverty line and those who own mechanized boats or
who are fish distributors are well above it. With this helpful poverty profile
in mind it is clear that a significant share of project benefits will not go directly
to those below the poverty line.
Coast stabilization benefits are described as those that arise from
the coastal land that will be saved with the stabilization of erosion due to
the project. The most important of these benefits are (i) the house values
saved, (ii) the opportunity cost of agricultural land [given by the per hectare
value of copra and paddy], and (iii) the impact on tourism [since tourism
arrivals and income are assumed to fall without the project]. Hence if the
poor do not own permanent houses on the coastal area, do not farm significant
plots and are not linked with tourism, they will not be expected to gain from
the net income generated through improved coast stabilization.
Coastal resource management benefits are in the form of the avoided
decline in resource value that would occur in the absence of the project; an
annual rate of decline of 1 percent in the without-project case is assumed.
Resource value here relates to the sustainable use of coastal products such
as mangrove vegetation based products and fisheries, with some allowance
for biodiversity and tourism benefits. Here the main link with the poor is
likely to be through lagoon fishing and some subsistence use of vegetationbased products.
The benefits from the harbor and fishery components are quantified
by the economic value of the incremental fish catch due to the project. The
RRP stresses that the primary poor beneficiaries will be fisherfolk, who will
face greater employment opportunities on boats and higher catches and
higher fish prices, through improved quality. A figure of 1,000 new jobs for
fishers is mentioned. However, how far these will be filled by those below
the existing poverty line is unclear and the main income gains from higher
Untitled-1
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Table A6.6
+ + +
>-
!
&<(
B
) ;
375,000
350,000
Fish distributors/wholesalers
businessmen
325,000
Multi-day boat owners/
fishers
:
300,000
Prawn farm owners
275,000
250,000
225,000
Trawler owner/fishers
200,000
Large fish processors
Traders
Trawler owner/fishers
Small fish plant owners
Landowners/businessmen
175,000
Boat renters
Boat renters
150,000
125,000
Lime kiln operators
boat repair operators
115,000
Government employees
Mechanized small-boat
fishers
100,000
85,000
moderate
income
earners
75,000
60,000
Untitled-1
Government employees
Prawn farmers
Small fish processors
Traditional/canoe fishers
Lagoon fishers
Coral miners
just
above
poor
116
5/17/02, 10:26 AM
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
117
) ; Resource management measures which will benefit all fishery-related residents in the
target communities and beyond
Infrastructure improvement program to enhance, expand, and upgrade landing place
facilities to improve product quality and provide new processing opportunities.
Environment protection awareness program
Training in milkfish aquaculture
Improvement in fish handling, storage, delivery, and marketing methods
Improvement in fish landing facilities
Transfer of appropriate fish processing technologies
Improvement of craft design including refrigeration of fish loads
Environment protection awareness program
Microenterprise development and credit assistance
Improvement of craft design
Skills training
Microenterprise development and credit assistance
Skills training
Microenterprise development and credit assistance
Skills training
Access to beach.
Provision of NGO/CBO support and extension
Training in milkfish aquaculture
Improvement of fish handling, storage, delivery, and marketing methods
Transfer of appropriate fish processing technologies
Environment protection awareness program
Alternative livelihood assistance and credit
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A6.6 .. cont’d.
+ + +
>-
!
&<(
B
) ;
Fish vendors
50,000
:
Lagoon fishers
Fishing boat crew
poor
40,000
30,000
Net repair persons
Small agriculture operators
Wage earners
Fish vendors
coral collectors
Mobile fish sellers
Mobile fish sellers
Part-time fishers
poverty
threshold
20,000
Canoe repairmen
Part-time fishers
Wage earners
Coir fiber/rope makers
15,000
Casual workers
Casual workers
Subsidy recipients
Subsidy recipients
10,000
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5/17/02, 10:26 AM
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
) ; Microenterprise development and credit assistance
Skills training
Provision of NGO/CBO support and extension
Improvement in fish handling, storage, delivery, and marketing methods
Transfer of appropriate fish processing technologies
Environment protection awareness program
Microenterprise development and credit assistance
Skills training
Environment protection awareness program
Microenterprise development and credit assistance
Skills training
Provision of basic social infrastructure, freshwater supply and latrines
Provision of NGO/CBO support and extension
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119
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A6.7
" ! -+ (SLRs million)
Present values at 12%
Total Costs
Total Benefits
of which coast stabilization
coastal environment management
fisheries and harbors
2,191
2,850
1,517
630
703
4
, %
"7
revenue from fishing are likely to accrue to boat owners and merchants. In
general a higher level of economic activity in the area, whether through higher
tourism or fishing income, can be expected to improve the position of the
poor to some extent, through a loose "trickle down" process, but this Project
does not appear as a particularly closely targeted poverty intervention.
What would be a useful further input into economic analysis of the
project from the RSA are data on income earnings for poor households from
different sources—such as small farming, fish vending, fishing and other
casual employment. This would allow estimates of how far the incomes of
the poor would rise with the Project and hence given the number of poor
households would allow estimates of their proportionate share in project
benefits and the numbers who would move out of poverty.
One can also use the wage cost data on fishing to estimate income
gains to newly employed fishers, if these are taken to come from the poor.
However, it should be remembered that logically the income gain to a new
worker is not the wage paid but the wage minus the worker's opportunity
cost. In this case a labor CF of 0.8 is used, which implies that only 20 percent
of the wage paid is actually an income gain to poor workers.
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APPENDIX 6 Case Illustrations of Poverty Impact Analysis
121
Case 4 – Lao PDR: Primary Health Care Expansion Project
The following is taken from the RRP. 4 EDRC staff participated in the
project preparation at the loan fact-finding stage. The economic analysis paid
special attention to financial sustainability (potential for cost recovery) and
fiscal affordability (to cover the project recurrent costs). While in health and
education projects financial sustainability continues to be a major concern,
quantification of economic efficiency (cost-effectiveness) in service delivery
and distributional impact will need more attention in order to demonstrate
poverty impact. The following is an attempt in this direction within data and
methodological constraints.
Project Description
The Project will build on the lessons learned from the first ADB-assisted
Primary Health Care (PHC) Project in the Lao People’s Democratic Republic
(Lao PDR) and expand its coverage from two to seven provinces in the North,
while at the same time improving the quality of services, as well as assisting
the Government in forming a nationwide PHC network. The Project has two
components. Component 1 will develop PHC in the northern provinces by (i)
increasing access to PHC at health centers and village levels; (ii) improving
the quality of PHC including training of ethnic minorities staff; (iii) strengthening
maternal and child health and family planning services; and (iv) supporting
village health care and promotion. Component 2 will strengthen the
institutional capacity for PHC nationwide by (v) strengthening PHC
coordination; (vi) standardizing management systems; (vii) supporting staff
development and training for PHC management; and (viii) testing innovative
financing approaches. The total project cost of $25 million is to be financed
by ADB ($20 million) and the Government ($5 million).
Economic Analysis – Cost-Effectiveness Approach
Measurement of economic benefits associated with preventive health
care could ideally accommodate a diverse set of benefits such as productivity
4
Untitled-1
Loan 1749-LAO (SF): Primary Health Care Expansion, for $20 million, approved on 24 August
2000.
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gains due to fewer life years lost to mortality and morbidity, consumption
gains due to a higher quality of life, and increased life expectancy and cost
savings on curative treatment. In theory, under certain restrictive assumptions,
the willingness of health beneficiaries to pay for health services will be the
appropriate measure of the benefits to society from these services. In practice,
however, partly due to the technical difficulty and often unjustifiably high
research costs of estimating unbiased willingness to pay, the most commonly
used approach is to focus on the production side of health effects. The economic
analysis for the first PHC project (appraised in 1995) estimated economic
benefits based on the expected gained lifetime income due to the reduced
under-5 mortality rate (U5MR). This method leaves out the benefits from
reduced morbidity and also the consumption value of improved health status.
A fuller discussion of this approach and its limitations is in the ADB Handbook
for the Economic Analysis of Health Sector Projects (2000). For the current
Project, cost-effectiveness analysis has been applied as a well accepted test
for health projects.
Cost-effectiveness of health interventions is often measured as cost per
DALY (disability-adjusted life year) saved. A DALY is a measure that combines
mortality, morbidity, and disability weighted for years of life saved at different
ages, with social preference in favor of working-age years reflected in the
implicit weights. Despite its limitation, DALYs provide a quantification of
the burden of disease comparable across alternative health interventions
and are increasingly used in developing countries. For the purpose of the
analysis, DALYs are used to provide an indication of the current burden of
disease from a selected set of medical conditions addressed by the Project.
For accuracy, DALYs should be calculated from data specific to the
country. The requisite data for these calculations is not yet available in the
Lao PDR, however, so a second-best approach for the estimation of DALYs
has been taken. A large international database of disease burdens for different
geographic regions and two large countries (People’s Republic of China and
India) is available (Murray and Lopez 1996). These data give total DALY lost
for each region for a large number of medical conditions, and DALYs are given
both in total and for different age groups. It is assumed that the Lao PDR has
similar epidemiological conditions to the region which also includes Thailand,
Cambodia, Viet Nam, and Indonesia (categorized in DALY database under
Other Asian and Islands Region). An adjustment was made for the population
size of the Lao PDR relative to this comparator region to obtain estimates for
DALYs lost by age group, sex, and medical condition for the Project provinces
Untitled-1
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5/17/02, 10:26 AM
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
123
Table A6.8
,A , 8
&
Hemorrhage
Sepsis
Eclampsia
Hypertension
Obstructed labor
Abortion
0.0056
0.0119
0.0015
0.0019
0.0077
0.0020
;
$ <.. (/
4& &
&
Pertussis
Poliomyelitis
Diptheria
Measles
Tetanus
Vitamin A deficiency
Perinatal conditions
0.0178
0.0057
0.0006
0.0583
0.0295
0.0351
0.1830
Diarrheal diseases
Respiratory Infections
Malaria
STDs excluding HIV
0.0517
0.0690
0.0089
0.0047
4&
<.
(//$
($/./
Source: Adapted from 1993 World Development Report, augmented by 20% to adjust for time lapse since the research year of 1990.
(again see ADB Handbook for the Economic Analysis of Health Sector Projects
for further information.)
The target population in the seven provinces is categorized into three
groups and their health conditions to be addressed by the Project
interventions are given for each group as in Table A6.8. Per person incidence
of DALY for each health condition is taken from the above source and adjusted
to the Project target population. The greatest difficulty in economic analysis
of health projects lies in the uncertainties of the reduction of disease burden,
or saving in the otherwise lost DALYs every year specifically accruable to
the Project. The incremental DALYs saved due to the Project also depend
on the assumed counterfactual scenario without the Project. In some cases,
where interventions involve price increases of public services, both consumer
response and private sector response must be taken into account to project
the future utilization of the project services and their associated health
impacts. Because PHC services are targeted primarily to the poor and the
new pricing policy on medical fees and consumables will not significantly
influence the target population's utilization of the PHC services. Here, the
"before-project" baseline health situation is simply taken as the "withoutproject" situation and different scenarios (low, moderate, and high) have
been prepared to test the robustness of cost-effectiveness indicators to
different effective PHC coverage of the target population. The low scenario
assumes that the percentage of the target population reached and full impact
of the PHC intervention on mortality and morbidity realized will gradually
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5/17/02, 10:26 AM
124
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
increase to 10 percent during Project implementation and stay so thereafter.
The moderate and high scenarios assume the percentage will gradually
increase to 15 percent and 20 percent, respectively, during the project
implementation period and stay so thereafter.
The Project life is assumed to be 15 years, beyond which the project
investments will cease to yield health benefits and leave no residual value.
Investment costs include construction and upgrading of health units and
hospital buildings, medical equipment, drugs and supplies, four-wheel drive
vehicles and motorbikes, various consulting services and researchers, and
training and materials. Recurrent costs include maintenance costs of buildings,
equipment and vehicles, and operational costs of supervising health care
workers and utilities and communications for PHC offices. Both financial and
economic costs per DALY saved are calculated. Financial cost per DALY saved
shows cost-effectiveness from the budgetary viewpoint, whereas economic
cost per DALY saved shows the impact on the economy in general. In economic
costing, the world price numeraire (US dollar denomination) was used. This
does not prove inconvenient for distribution analysis as the Project benefits
will not be valued. In converting the financial prices of nontradable goods
into their economic costs, a standard conversion factor (SCF) of 0.9 was used.
Estimation of a shadow wage rate factor (SWRF) was problematic as the official
unemployment stands at only 0.9 percent (primarily due to high prevalence
of self-employment in subsistence agriculture). Considering this figure is close
to unity and the uncertainty over the exact unskilled labor content in the
lump-sum contract of civil works, SWRF was not used in economic pricing
of labor costs. As is obvious in the case of the Project areas in the Lao PDR,
the cost of time to the nearest health service unit is borne by the beneficiaries
and should be valued as additional economic costs. However, the analysis
here did not include such costs mainly due to the lack of detailed data on the
quantity and value of the opportunity cost of travel time. Based on these
assumptions, a 3 percent discount rate was used in discounting project costs
and output stream (DALY saved), as discussed in the Handbook for the
Economic Analysis of Health Sector Projects (ADB 2000), to enable an
:
$ <2 Untitled-1
4
$ <2 Low Scenario:
$49.02/DALY
Low Scenario:
$45.82/DALY
Moderate Scenario:
$30.08/DALY
Moderate Scenario:
$28.11/DALY
High Scenario:
$22.56/DALY
High Scenario:
$21.09/DALY
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5/17/02, 10:26 AM
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
125
international comparison of different health interventions. The results are as
follows.
ADB's Policy for the Health Sector lists costs per DALY saved for some
comparable health interventions: immunization at $25, family planning at $2575, malaria prevention using impregnated bednets at $15–20. The high-scenario
cost is considered well within the acceptable range compared with this
international norm. The low-scenario cost is much higher than the international
comparator, which may be explained by the assumptions regarding the cost
of services, in particular family planning services, which appears quite high,
and the geographical condition in the Lao PDR. This would justify a relatively
higher cost per DALY saved.
Poverty Impact Analysis
Poverty Incidence
The PPTA household survey chose rice shortage (whether the household
had enough rice during the previous year) as an indicator of poverty. Field
surveys found that poverty in rural villages in the Lao PDR in subsistence
production is mainly linked with lack of workforce and land shortage. Lack
of both combined is best reflected in rice shortage. But this does not allow
comparison between provinces and regions. As of this writing, the Government
of Lao PDR has not yet decided on an official poverty line. Two alternative
lines developed by the World Bank and the Swedish Statistics Office give
conflicting information on poverty incidence. The latest ADB study (Kakwani
et al. 2001) attempted a calculation of a poverty line that is consistent between
the 1992/93 Lao Expenditure and Consumption Survey (LECS1) and the 1997/
98 update (LECS2), based on which the average total poverty line for the Lao
PDR is estimated to be 19,184 kips per person per month in 1997/98 prices
(about $9 as opposed to the World Bank criterion of $14). Taking this poverty
line for the current analysis, poverty incidence in the target provinces is
indicated in Table A6.9.
Out of the total target population, about 1 million people will be under
direct project influence. Using the 1995 census data, about 155,000 (14.9
percent) will be children under five years of age and 209,000 (20 percent) will
be women of reproductive age. These two groups will be the major beneficiaries
of the reduced burden of diseases due to the Project. By ethnicity, the population
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126
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
)"7 77/777#/7*'$-
,0-B3 Bokeo
Louang Namtha
Phongsali
Houaphanh
Oudomxai
Xieng Khouang*
Louang Prabang
Sayaburi
Northern Region
< 63.5
60.3
68.7
78.4
51.1
57.3
62.7
30.1
58.4
+
37.4
57.5
64.2
74.6
73.2
34.9
49.4
21.2
52.5
*"
-10.6
-1.0
-1.3
-1.0
7.2
-9.9
-4.8
-7.0
-2.1
C
0 out of 5
2 out of 5
3 out of 7
5 out of 6
2 out of 7
1 out of 7
2 out of 10
0 out of 10
14 out of 50
**
Source: Kakwani et al. (2001). Note: Xieng Khouang province belongs to Central Region.
directly covered by Component 1 of the Project will be distributed as 334,700
persons (35.6 percent) to the Tai-Kadai group, 336,600 persons (35.8 percent)
to the Mon-Khmer group, 163,400 persons (17.4 percent) to the Hmong-Mien
group, and 106,400 persons (11.3 percent) to the Sino-Tibetan group. Using
the poverty incidence above for the Northern Region as an approximation,
the poor beneficiaries out of the target population will be about 53 percent.
In Louang Prabang, the Project will work mainly at the provincial hospital
level for this component. Although the district level population and below
will certainly benefit indirectly, these are excluded from the direct beneficiaries
as a conservative estimation.
Differential Project Impact
The percentages of the poor in the above table can be considered as
the first order approximation of the poverty impact ratio on a headcount
basis, that is, 53 percent of the project benefits are considered to go to the
poor. But this simple interpretation is based on an unlikely assumption, that
is, neutrality in the burden of diseases falling proportionately on the poor
and nonpoor. In reality, it is very likely that the Project will benefit the poor
more than this number indicates for the following reasons.
(i) Disease is distributed to the poor more proportionately than to
the nonpoor. A probit analysis using the PPTA household survey
data shows that the poor (defined as those experiencing rice
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APPENDIX 6 Case Illustrations of Poverty Impact Analysis
127
shortage) are 28 percent more likely to have suffered from any
illness in the previous month than the nonpoor, and 21 percent
more likely to have suffered from severe illness. Therefore, DALYs
saved per unit of Project service will be distributed more than
proportionately to the poor than the headcount indicator suggests.
More detailed research on differential health problems for the poor
and nonpoor would have allowed a clearer projection of the
distribution of DALYs saved.
(ii) On the other hand, the same analysis indicated no clear differences
between poor and nonpoor in health-care-seeking behavior for
various health providers and health goods expenditure. This
indicates that the Project services, especially preventive and
promotive services, will be equally utilized by both the poor and
nonpoor.
(iii) The analysis found that poverty is not linked with the degree of
remoteness of villages. Every village has its poor households and
poverty is linked to social conditions (addiction, single parents,
age profile, relatives) and land availability in the individual
households. Each village is likely to have some households who
cannot produce enough and are trapped in poverty and lack of
food. An effort to reach the very poor would therefore need to
target individual households, in addition to some specific and small
ethnic minorities. Subsidies (or differential user charges) can be
used precisely for this purpose.
(iv) Regarding first-level curative care, many patients come to
provincial/district hospitals from far distances simply due to the
unavailability of the basic service in their locality and in many
cases spend significant economic costs to arrive there. The
Project services at lower level health facilities will create access
for the poor who cannot currently afford the travel cost and this
effectively constitutes an economic subsidy targeted at the poor.
There is also an evidence for a differential impact of health interventions
between rural and urban areas. A PPTA study for Viet Nam (Dunlop 1999)
attempted to quantify the distribution of DALYs lost. It found that burden of
disease among the poorest rural young children is more than 27 times the
rate experienced by those in urban areas. For the entire population, the poor
rural population has four times as great a disease burden as urban dwellers.
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128
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
As this Project is also exclusively targeted to northern rural provinces, the
same differential impact will benefit the poor disproportionately. Due to the
likely underestimation of poor beneficiaries, it could be that their share in to
the total is close to 75 percent, which can be seen as a target for international
best practice for poverty-focused interventions.
Case 5 – Tajikistan: Road Rehabilitation Project
The following is based on the RRP 5 and the associated PPTA consultant
report on poverty impact analysis. Clear terms of reference for the consultant
were prepared to carry out a separate distribution and poverty impact analysis
to extend the preceding conventional economic analysis. A good attempt was
made to collect primary data through field surveys in order to identify and
disaggregate beneficiaries of road infrastructure. A good judgment was
applied to the extent of the use of the secondary information from the existing
household survey data. A sensitivity analysis was carried out to relate the
importance of the market structure of road transport services to the poverty
impact of the project.
Project Description
Tajikistan is the poorest of Central Asian republics. The breakup of
the former Soviet Union resulted in a dramatic decline of economic activity
and increase in poverty. Rehabilitation of the country's road infrastructure
is necessary to sustain the economic recovery now under way. The scope
of the Project includes rehabilitation of approximately 80 km of the most
deteriorated sections of the Dushanbe-Khulyab road and improvement of
approximately 150 km of rural roads including grading dirt road surfaces and
building appropriate drainage facilities. Of the total project cost of $26.8 million,
$20 million is to be financed by ADB, $4 million by OPEC Fund, and $2.8 million
by the Government.
5
Untitled-1
Loan 1819-TAJ(SF): Road Rehabilitation, for $20 million, approved on 20 December 2000.
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5/17/02, 10:26 AM
APPENDIX 6 Case Illustrations of Poverty Impact Analysis
129
Economic Analysis
The economic analysis was carried out on the basis of a comparison
of the with- and without-project scenarios. Without the Project, the road would
receive only minimal maintenance and continue to deteriorate. With the Project,
routine and periodic maintenance will be provided according to international
standards. The analysis covers a period of 24 years (2001–2024), including
about 4 years for project implementation. All benefits and costs were estimated
in constant 2000 prices. With no revenues involved and no financial analysis
carried out, it was decided that economic pricing be done using the world
price numeraire. It would be normally simpler to use domestic price numeraire
if distribution analysis is envisaged, but due to the uncertainty of currency
exchange rates, project cost estimation has been done in US dollars, therefore,
this is a case where the choice of world price numeraire is simpler throughout
the analysis. A standard conversion factor (SCF) of 0.9 was applied to the
valuation of nontradable inputs and a shadow wage rate factor (SWRF) of
0.75 was applied to valuation of unskilled labor.
The Project's economic benefits are primarily derived from savings in
vehicle operating costs (VOCs) in normal traffic, passenger time savings, and
benefits arising from generated traffic (VOC savings estimated as half the
value for normal traffic). The Project will indirectly improve the access of
the rural poor to markets and social services, and improve safety conditions
on the road. However, sufficient data are not available to quantify these
benefits and they were not included in the analysis. Unit economic VOCs
for passenger and freight vehicles were estimated using the highway design
and maintenance model (HDM-Manager). VOC savings will accrue primarily
from improvements in road surface, horizontal and vertical alignment, and
increased average speed in some sections. The estimation result showed
savings in VOCs comprise the largest category of benefits, accounting for
more than 85 percent of the total benefits. EIRRs were calculated for the
national road sections and for the overall Project. The EIRR for the overall
Project is 15.9 percent while the EIRRs for the national road sections range
from 14.2 to 25.4 percent. Sensitivity analysis is omitted here.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Distribution Analysis
The distribution analysis followed the following steps.
(i) Disaggregation of road user benefits: (VOC and time savings)
between passenger and freight vehicles: This was done by going
back to the base data on traffic forecasts and road user unit costs
identified for 4 classes of passenger vehicles and 3 classes of freight
vehicles.
(ii) Field surveys to inform road user benefit incidence: In order to
inform the distribution analysis and also to assess the structure
and performance of the transport market in Khatlon Oblast and
the project area, the consultant designed 3 sets of questionnaires
and undertook field surveys for (a) passengers, (b) drivers, and
(c) farmers. The drivers survey was designed to learn about the
structure of the bus and trucking markets and the extent of direct
Government involvement in providing transport services. The
passengers survey was designed to learn about fare structures
for the various modes of transport and the degree to which
passengers encounter problems with access to common
destinations such as hospitals and schools. The farmers survey
was designed to learn about freight usage of road infrastructure
and its service cost. Income questions were included in all surveys
to broadly capture the user profile. These surveys helped determine
the degree to which road user benefits are passed on from vehicle
owners to users, and the degree to which the poor and the
extremely poor will benefit from the project and what institutional
constraints bar the poor from receiving a larger share of the
benefits from the Project.
Survey results: The market for passenger transport services on
the project road and area is fairly competitive. Small owneroperators dominate 80 percent of the market, making it
substantially competitive. Drivers' reported incomes and household
expenditures fit the national pattern and this suggests that they
are not able to extract much, if any, from monopoly rent from
owning and operating vehicles on the project road. For freight
services, farmers are big users of the project road. They prefer to
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APPENDIX 6 Case Illustrations of Poverty Impact Analysis
131
sell the output themselves because they are not satisfied with
the prices offered by the middle marketers. Of those reporting to
sell locally, 80 percent cited high transport costs (averaging about
20 percent of the output price) as the reason for not going to more
distant markets. The surveys revealed that several institutional
barriers prevent both transport and agricultural markets from
being more competitive. These barriers will prevent the full
benefits from accruing to the users of the transport systems. First,
Government-owned bus terminals set fares for those buses using
the terminals. The bus terminals are large and centrally located
in all the major cities of the project area. Aside from blocking
drivers from passing on cost savings to passengers, this pushes
the transportation markets out of the bus terminals. The most
efficient solution would be for the bus terminals to set rental fees
for the bus bays at a low enough level to attract the buses away
from the "informal" high-cost markets to bus operators and
passengers. Second, outside Dushanbe, state-owned truck
companies are reluctant to lease vehicles. The surveys showed
that outside the capital, there are no leased trucks or buses from
the Government. Allowing drivers to lease vehicles will increase
the supply of operating vehicles that are free to seek market
business, as opposed to waiting at stations for calls to move freight
at Government-set prices. Third, along the project road, the state
motor vehicle inspectorate (GAI) personnel operate informal toll
stations. Drivers reported being stopped at an average of 5 GAI
checkpoints while traveling their most frequent route. At each
checkpoint, they were required to pay an average of Somoni
(TJR)517. These tolls do not revert to the Government budget in
any form.
(iii) Use of secondary data: The consultant supplemented the above
information with the results from a large survey implemented by
the State Statistical Agency (SSA) in June 1999, known as the
Tajikistan Living Standards Survey (TLSS). It was designed as a
stratified random sample of 2,000 households covering 14,000
individuals, and is representative of the country. Together with
SSA, the consultant computed special tabulations for Khatlon
Oblast for the purpose of the Project. The sample size for the Oblast
is sufficiently large to allow valid statistical inferences in most
cases. The TLSS data allowed estimation of income elasticity of
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
demand for transport for Khatlon Oblast. The result shows that
the demand is income elastic (about 1.2). The implied income
elasticity of demand for gasoline for transportation is even more
elastic (about 1.6), implying traffic growth will be somewhat
dominated by passenger vehicles. The TLSS collected extensive
agricultural data, but the quality of the data is not sufficient to
draw inferences, for example, on agricultural price elasticities of
supply. Moreover, due to the dominance of state-owned farms in
the Oblast and the lack of transparency in the transmission of
revenue from crops sold and farm workers' incomes, especially
cotton, computation of such elasticities is not meaningful at this
time.
(iv) Assumptions for distribution of road user benefits: Based on the
above steps, the road user benefits that will be passed on to users
were estimated at 20–50 percent depending on the vehicle type.
The 50 percent estimate for passenger cars and buses reflects
the consultant's judgment that these markets are competitive. The
20 percent estimate for pickups and articulated trucks reflects
the paucity of data and information on the use of these vehicles
in the project area and some conservative judgment. Since 1998
there has been a significant de facto privatization of the vehicle
fleet in Tajikistan. Therefore, the consultant's estimates on
Government ownership across commercial vehicle classes are
much lower than the 1998 data supplied by the Ministry of
Transport and Roads. Also, based on the field survey, the consultant
estimated the share of Government-owned noncommercial
vehicles as small. Passenger cars and pickups remain largely in
the private sector. Leasing of state-owned vehicles is only
permitted for state fleets in Dushanbe. Therefore, it is estimated
that a small share of the state's fleet of trucks and buses is leased,
and that over half of the benefits from leasing the state vehicles
accrue to the Government.
Using the above information and assumptions, all economic net benefits
are distributed among the following categories of stakeholders: (i) passenger
users, (ii) freight users, (iii) vehicle owners, (iv) labor, and (v) Government/
economy. As some of the Project's capital expenditure and maintenance will
be spent on local labor, unskilled laborers will gain net benefits to the extent
that their paid wages are higher than their opportunity cost of labor (reflected
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APPENDIX 6 Case Illustrations of Poverty Impact Analysis
133
in SWRF). As there are no toll roads or other mechanisms for direct cost recovery
from road users, the Government (and the rest of the economy) will be the
sole bearer of the economic resource costs associated with capital expenditure
and maintenance. Because the initial scope of the Project analysis did not
include rural feeder roads and the traffic forecast and associated VOC data
were generated only for the trunk road, the analysis here is also limited to
that of the trunk road. The summary result of the distribution analysis is
presented in Table A6.10. The total net benefits of about $8 million (in present
value discounted at 12 percent) does not include nonquantifiable but indirect
benefits such as increased trade and economic activity that are induced by
the improved road. The size of these indirect benefits depends on how the
regional and national economies grow over the Project's time horizon.
As discussed in Case 1 of this appendix, to make the calculation
consistent with the world price numeraire, financial flow data must be multiplied
by the SCF (even when the accounting currency unit is US dollars) before
starting the distribution analysis. In the current case, however, because the
original financial data was not included in the consultant report, the financial
flow figures in Table A6.10 have been derived by dividing economic flow figures
by the SCF. Therefore, the result is not as accurate as it could have been, but
the errors are unlikely to be important.
The public lender column was added to the original RRP primarily to
make a point that in principle, the gain/loss to the aid agencies can be
accommodated in the distribution table. The numbers associated with lending
arrangements in this example (underlined numbers in Table A6.10) are artificial
due to the lack of data on loan schedules for OPEC lending. The procedure
to calculate these numbers would be to discount loan disbursement and
principal and interest payment flows both at the opportunity cost of capital
of 12 percent. In the example below, the table shows that the public lender
(ADB and OPEC) is a net loser and its net loss is matched by the equivalent
amount of gain accruing to the project operation represented by the financial
present value column. The intent of such refinement can be taken to illustrate
that aid agencies by definition incur net loss for every loan they lend and
present this point explicitly in the distribution analysis. However, this
refinement does not change the aggregate project net benefits, or the poverty
impact ratio. This result arises when we assume that funds from aid agencies
are channeled through the government financing pool for public investment,
and therefore, the gain/loss to the aid agencies can be pooled together with
the gain/loss to the government/ economy. Also, if the analyst prefers not to
show the distributional details associated with project financing, the columns
Untitled-1
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Untitled-1
134
(3,417)
(195)
0
0
8043
(3,797)
(260)
(500)
(2,000)
(2,760)
380
65
500
2,000
10,803
11,655
(3,797)
Economic
Minus
Financial
Gains and Losses
Proportion of the Poor (%)
Net Benefits for the Poor
11,655
0
0
3,797
Economic
Present
Value
4,130
60
2,478
4,130
4,130
Passenger
Users
1,947
60
1,168
1,947
1,947
Freight
Users
5,578
30
1,673
5,578
5,578
Vehicle
Owners
65
80
52
65
65
500
2,000
(1,297)
(3,797)
Public
Lender
Poverty Impact Ratio
Labor
Note: The net loss to Public Lender and Government/Economy (-3,677) is calculated as -2,760-1,297+380.
"
#
Road user
benefits
Loan
#
Investment
and O&M
Labor
Interest
Principal
$
%
Financial
Present
Value
(3,677)
10
(368)
380
380
Govt/
Economy
Table A6.10
! / $B%=333 %& (
5,004
0.62
8,043
Total
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APPENDIX 6 Case Illustrations of Poverty Impact Analysis
135
for the public lender and the government/economy can be collapsed into one
column, in which case the underlined numbers disappear but the rest of the
table remains unaffected (this can be easily verified in the table).
Poverty Impact Analysis
In moving from the above distribution analysis to poverty impact analysis,
the following assumptions were made.
(i) Passenger users: In the consultant's survey, outside Dushanbe,
60 percent of drivers, farmers, and passengers using the
transportation services in the project area are poor, using the
poverty line estimated in the TLSS. Based on the available
information, experience from other disadvantaged transition
economies, and expert judgment, this number can be taken to
be the percentage of the net benefits for passenger users
accruing to the poor.
(ii) Freight users: Poor farmers are the most common users of freight
transport in the Oblast and the project area. Again, based on the
survey result (e.g., farmers lose about 20 percent of their produce's
value due to low quality road conditions), other available
information and expert judgment, the consultant estimated that
60 percent of the net benefits for freight users will accrue to the
poor.
(iii) Vehicle owners: According to the consultant's survey, the poor
use taxis for transport to some extent and the TLSS data support
private passenger vehicle ownership among the middle three
household per capita expenditure quintiles. The consultant
estimated that 30 percent of the net benefits to vehicle owners
will accrue to the poor.
(iv) Labor: Given that the project road will be rehabilitated using
unskilled labor, among which a great majority come from the
poorest to poor households, the consultant estimated that 80
percent of the benefits to labor will go to the poor.
(v) Government/Economy: The TLSS documents an extremely high
headcount poverty incidence (over 80 percent) and using the
information in the TLSS leads to an equally extremely high income
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
share of the poor in GDP, compared to other DMCs. Considering
the uncertain quality of household expenditure data, the
fluctuating nature of the economic transformation under way in
the short run, and the long project life by comparison, this number
seems to provide little reliable clue at the moment as to the
incidence of the net government/economy loss. Here simply the
rule of thumb of 10 percent is applied as a base case.
As in Table A6.10, the poverty impact ratio (PIR) based on the above
assumptions is estimated at 0.62. While the result of the TLSS points to the
extremely high poverty incidence nationwide on a headcount basis, it is
difficult to make a sensible judgment as to where a national reference point
should be to compare with the PIR.
Due to somewhat uncertain assumptions, sensitivity analysis has been
performed on some key parameters. First, the PIR sensitivity can be tested on
the poor proportion of the government/economy net loss. Under a scenario
in which the extreme poverty situation suggested by the TLSS result
continues over a good part of the project life, let us use 0.5 instead of 0.1
for this parameter. Then the PIR is 0.44, which is lower than the base case,
now due to the assumption that a higher proportion of the government net
loss will be borne by the poor outside the project influence area. Second, we
can assume a case where transport services markets are less competitive
than the consultant's base case assumption and stay that way. Instead of
the 20–50 percent assumed above for the road user benefits passed on from
vehicle owners to users, if we assume 10 percent for these parameters across
all vehicle types, the PIR is reduced to 0.44. Third, we can also assume a
case where transport services markets become much more competitive than
the consultant's base case assumption and where drivers and vehicle owners
act more like price takers than price setters. If we assume that they pass
on 85 percent of their cost savings to passengers and freight users in the
form of lower fares and freight rates, the PIR is increased to 0.80. This
illustrates the importance of enhancing competition in the transport services
markets not only to fuel economic growth but also to help reduce poverty
through road infrastructure.
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APPENDIX
7
Approximation of Income Share of the Poor
The following summarizes briefly how country-specific poverty survey
data can be used to derive a simple income share estimate for the poor; that
is, the share of current income going to those below the national poverty line.
These figures are based on the headcount poverty incidence – HC (share of
poor in total population) and the poverty gap – PG (average divergence of
income of the poor from the poverty line) estimates produced by poverty
surveys. With this data the income share estimates for the poor are derived
from the following formula:
Yp
=
=
((1-PG)*PL*POP*HC) / GDPpc*POP
((1-PG)*PL*HC) / GDPpc
Where Yp is the income share of the poor,
PG is the poverty gap index,
PL is the poverty line annual income per capita ,
POP is total population,
HC is the headcount poverty index, and
GDPpc is GDP per capita.
As an example, a calculation for Viet Nam is illustrated as follows. The
1999 poverty survey on Viet Nam, "Attacking Poverty," provides the necessary
poverty data. Strictly the report uses expenditure rather than income basis
for poverty, but here we implicitly assume zero savings by the poor, so that
all income is spent and the terms can be used interchangeably. For 1998 the
year of the survey, the national poverty line is set at 1,790,000 dong. This is
not directly comparable with the international $1 per day figure, since it is
in nominal, not purchasing power parity prices. This is the higher of the
two poverty lines referred to in the report, since the lower line covers only
food essentials in the basket of goods consumed by the poor. At this poverty
line the proportion of the population in poverty is estimated to be 37.4 percent
(so that HC = 0.374). On the other hand, the poor tend to be bunched around the
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Table A7.1
C (based on country-specific p
Table A7.1
C (based on country-specific poverty lines)
$
2
Azerbaijan
Bangladesh
Urban
Rural
Cambodia
Phnom Penh
Other Urban
Rural
India
Urban
Rural
Indonesia
Urban
Rural
Kazakhstan
Kyrgyz Rep.
Lao PDR
Mongolia
Nepal
Pakistan
Philippines
PNG
Sri Lanka
Urban
Rural
Tajikistan
Viet Nam
1995
8
<
B
-
41
41
n.a.
n.a.
0.615
14,796
7,896
0.263
0.511
575,970
461,360
407,705
0.111
0.299
0.401
353
266
4,236
3,192
0.256
0.306
1,109,952
891,264
44,592
4,647
23,028
86,880
4,404
3,552
10,284
461
0.194
0.260
0.434
0.510
0.617
0.365
0.420
0.340
0.367
0.375
11,400
11,400
240,000
1,790,000
0.250
0.410
0.826
0.374
1995
1995
Taka
Taka
1,233
658
1996
1996
1996
Riel
Riel
Riel
1 , 5 7 8/day
1 , 2 6 7/day
1 , 1 1 7/day
1996
1996
Rupee
Rupee
1999
1999
1998
1997
1997
1995
1995
1991
1998
1996
Rupiah
Rupiah
Tenge
Som
Kip
Tugrik
Nep Rupee
Pak Rupee
Peso
Kina
92,496
74,272
3,716
1995
1995
1998
1998
SL Rupee
SL Rupee
Somoni
Dong
950
950
20,000
19,184
7,240
296
Note: n.a. indicates either not available or judged not applicable.
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>
$
!
1
5/17/02, 10:26 AM
APPENDIX 7 Sample Terms of Reference and Resource1 Requirements
Introduction
Table A7.1
ountry-specific poverty lines)
;
!
1
Untitled-1
0.243
1997 WB Poverty Assessment
0.060
0.141
2000 BinayakSen report to ADB
same as above
0.022
0.075
0.097
1996 WB/1999 WB Pov. Ass.
same as above
same as above
0.075
0.093
2000 ADB/K.R. Mohan, GOI
same as above
0.020
0.018
0.128
0.180
0.138
0.109
0.121
0.071
0.130
0.124
2000 ADB Poverty Assessment
0.060
0.110
0.358
0.095
;
&5 E
!
(
D
- ; &'(
n.a.
12,720
12,720
28.8
27.2
653,541
653,541
653,541
9.6
19.5
22.6
12,820
12,820
n.a.
n.a.
2000 UNDP HumanDevReport
1999 WB Poverty Assessment
1997/98 LECS2/Kakwani2000
1996 WB Poverty Assessment
1997 WB Poverty Assessment
1995 WB Poverty Assessment
1998 PovSurvey/Kakwani2000
1996HIES/1999WB
5,377,548
5,377,548
114,781
6,445
455,633
239,345
10,776
9,211
35,633
1,654
n.a.
n.a.
14.7
30.2
26.9
11.8
15.1
12.2
9.2
9.2
2000
2000
1999
1998
36,572
36,572
167,669
4,790,558
7.3
11.4
n.a.
12.6
139
ADB Poverty Assessment
ADB Poverty Assessment
TLSS
VLSS
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139
139
140
H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
poverty line so that the average income shortfall of the poor is roughly 9.5
percent below the poverty line (PG = 0.095). In 1998 nominal prices GDP
per capita was 4,790,558 dong. Population data are not necessary in the
calculation as they cancel out in the above formula. Putting these values
into the formula gives us an estimated income share of the poor of
approximately 12.6 percent of GDP.
Yp
=
=
(1 - 0.095)*1790000*0.374 / 4790558
0.126
Similar calculations have been carried out for DMCs where data have
been collected from recent country-specific surveys as shown in Table A7.1.
However, note that the results are only indicative as they are based on the
existing poverty surveys and subject to changes depending on the specific
poverty lines to be agreed between DMCs and ADB. Also, there may be a
discrepancy between the expenditure survey data and national account data.
For example, it is well known that the survey-based expenditures are
underestimated in India and Indonesia compared to the estimate from the
national account exercise. For these two countries, in particular, it is
suggested that the 10 percent rule-of-thumb be used for this parameter. As
the preliminary calculations here used only the secondary sources indicated,
country-specific discrepancy check will require going back to the original
survey data. The values for parameters should be adjusted accordingly as
raw data become available.
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1
APPENDIX
Introduction
141
8
Sample Terms of Reference and Resource Requirements
Sample Terms of Reference for Poverty Impact Analysis under PPTA
Working closely with the Team Leader and in coordination with Social
Analyst, the Poverty Impact Analyst should undertake the following tasks.
(These tasks could be combined into the currently used terms of reference
for project economists. Also, sector-specific considerations could be added
(Table A8.1).
Review the questions addressed and data collected during the Initial
social assessment (ISA). Determine what information and data need to be
collected during the PPTA in order to carry out a reasonable poverty impact
analysis. See ADB Handbook for Integrating Poverty Impact Assessment in
the Economic Analysis of Projects (2001) (referred to as the Handbook hereafter)
for terms, methods, recommendations and available case illustrations.
Prepare a poverty profile of the project influence area. Clearly define
the poverty line to be used in the analysis. Use the line agreed in the Partnership
Agreement on Poverty Reduction between ADB and the DMC if already
available. Indicate the poverty incidence and characteristics of poverty and
perception of the people in the project influence area. In doing this, collect
and use as much available secondary survey data and information as possible
(see Appendix 3 of the Handbook).
Provide a profile of the quantity, quality, and prices of project services
available to different socioeconomic groups in the project influence area,
particularly to the poor. Identify the potential beneficiary (and/or loser) groups
separating those below the defined poverty line. Describe the current status
of the target population either in income/consumption or more general
socioeconomic characteristics. Examine the need or demand for the project
by the target population, and where appropriate, assess how much they are
willing to pay or forego to receive its services.
Provide a statement of the project features relating to poverty reduction,
including poverty-reducing measures, such as community participation,
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
29%
D ) Hefei-Xian
Railway
Following the Guidelines for
the Economic Analysis of
Projects
January 2000 (variation
2 during TA which started
in September 1999)
Guangxi
Highway
Development
Following the Guidelines for
the Economic Analysis of
Projects (for main highway
PLUS provincial and county
roads).
April 2000 (TOR variation
during contract
negotiations)
Uzbekistan
Railway
Modernization
Specific TOR: same as
Tajikistan plus "status of
separation of social
services” and PRA in 5
communities
June 2000 (variation 3
during TA which started in
October 1999)
Tajikistan Road
Rehabilitation
Project
Clear TORs for the DA/PIR
June 2000 (variation
during TA which started in
September 1999)
3
DA = distribution analysis; PIR = poverty impact ratio; PRA = poverty rapid appraisal; TA = technical
assistance; TOR = terms of reference
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Introduction
APPENDIX 8 Sample Terms of Reference and Resource1 Requirements
$
: F 1 transport economist
(intl.)
1 social analyst (intl.)
2 weeks
US$25,000
US$25,000
2 weeks
Total: 1 month (intl.)
1 transport economist
social analyst (survey)
143
143
2 weeks
1 day
Total: 23,500
(4% of max. contract
payment – US$600,000)
US$20,000
Total: US$10,000
Total: 0.5 months (intl.)
(2% of max. contract
payment – US$535,000)
1 social analyst (intl.)
1 social analyst (dom.)
1 economist-poverty
specialist (intl.)
1 economic/financial
specialist (intl.)
1 social analyst (dom.)
4 weeks
6 weeks
US$19,000
US$1,6000
Total: 1 month (intl.),
1.5 months
(dom.)
Total: 21,400
1 month
n.a.
1 month
n.a.
1 month
US$750
Total: 2 months (intl.),
0.25 months (dom.)
Total US$40,000
(5% of max. contract
payment – US$415,000)
(5% of max. contract
payment – US$834,000)
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
considered at project design and implementation stage. Explain the poverty
impact of the various alternatives; this can be quantitative (e.g., number of
jobs) or qualitative where quantitative data are lacking (e.g., access to
improved facilities). Justify the alternative selected on both economic
efficiency (e.g., higher economic NPV) and poverty reduction grounds (e.g.,
cost per unit of net benefits received by the poor.)
Carry out distribution and poverty impact analysis as outlined in the
Handbook and estimate poverty impact ratio (PIR). Spell out the critical
assumptions and data sources required to estimate the PIR. Identify the key
parameters that include uncertainties and apply sensitivity analysis on the
PIR, in addition to the currently practiced sensitivity analysis on FIRR/EIRR.
Discuss the extent of the reliability of the PIR estimation and qualify the result.
Explain the risks of failure to achieve poverty objectives. Examine scope
for leakage of benefits to nonpoor. Consider the possibility of project
encountering financial difficulties. Identify any complementary activity that
would reduce this risk.
Identify relevant (i) indicators, (ii) methodology, (iii) plan and timing,
(iv) location, and (v) human resource requirements to undertake monitoring
and evaluation of poverty impact in the project influence area. Ensure that
these are introduced in the Project/Program Framework for the proposed project
and in the Project Performance Management System (PPMS).
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Glossary
Average incr
emental economic cost (AIEC). The present value of
incremental
investment and operation costs at economic prices, divided by the present
value of the quantity of output. Costs and output are calculated from the
difference between the without project and with project situations, and are
discounted at the economic opportunity cost of capital (EOCK).
Benefit-cost ratio (BCR). The ratio of the present value of the economic
benefits stream to the present value of the economic costs stream, each
discounted at the economic opportunity cost of capital. The ratio should be
greater than 1.0 for a project to be acceptable.
Constant prices. Future price values from which any expected change
in the general price level is removed. When applied to all project costs and
benefits over the life of the project, the resulting project statement is in constant
prices. Expected significant changes in relative prices, that is, in expected
price changes for an item compared with the expected change in the general
price level, should also be incorporated in the valuation of costs and benefits
at constant prices.
Conversion factor (CF). Ratio between the economic price value and
the financial price value for a project output or input, which can be used to
convert the constant price financial values of project benefits and costs to
economic values. Conversion factors can also be applied for groups of typical
items, such as petrochemicals or grains; and for the economy as a whole, as
in the standard conversion factor or shadow exchange rate factor. For details
of CF estimation, see ADB Guidelines for the Economic Analysis of Projects
(1997).
Disability A
djusted Life Y
ear (D
AL
Y). A composite health impact
Adjusted
Year
(DAL
ALY).
indicator that combines morbidity and mortality impacts. Its calculation
requires two sets of weights: first by extent of disability and second, by
age for premature death and disability effects. For details, see ADB Handbook
for the Economic Analysis of Health Sector Projects, August 2000, or World
Development Report 1993, World Bank.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Discount rate. A percentage rate representing the rate at which the
value of equivalent benefits and costs decrease in the future compared to
the present. The rate can be based on the alternative economic return in
other uses given up by committing resources to a particular project, or on
the preference for consumption benefits today rather than later. The discount
rate is used to determine the present value of future benefit and cost streams.
Economic internal rate of return (EIRR). The rate of return that would
be achieved on all project resource costs, where all benefits and costs are
measured in economic prices. The EIRR is calculated as the rate of discount
for which the present value of the net benefit stream becomes zero, or at
which the present value of the benefit stream is equal to the present value
of the cost stream. For a project to be acceptable the EIRR should be greater
than the economic opportunity cost of capital.
Economic oppor
tunity cost of capital. The real rate of return in economic
opportunity
prices on the marginal unit of investment in its best alternative use. This rate
of return is estimated as the weighted average of the economic demand and
supply price of capital, and therefore will be equal to the value of the marginal
unit of investible funds to both investors and savers.
Equalizing discount rate (EDR). The discount rate at which the present
values of two project alternatives are equal. It is the same as the internal
rate of return on the incremental effects of undertaking an alternative with
larger net costs earlier in the net benefit stream rather than an alternative
with lower early net costs. The EDR is compared with the economic
opportunity cost of capital to determine whether the alternative with larger
net costs is worthwhile. Also referred to as the crossover discount rate,
the discount rate above or below which the preferred alternative changes
from one to another.
Factor intensity
intensity.. It refers to the degree to which a certain production
factor (e.g., labor, capital, land) is used in production and characterizes the
type of production technology. For example, labor-intensive technology (e.g.,
garment industry) refers to a production process in which more labor inputs
are required than capital inputs relative to capital-intensive technology (e.g.,
machinery industry).
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G LOSSARY
147
Financial internal rate of return (FIRR). The rate of return that would
be achieved on all project costs, where all costs are measured in financial
prices and when benefits represent the financial revenues that would accrue
to the main project participant. The FIRR is the rate of discount for which the
present value of the net revenue stream becomes zero, or at which the present
value of the revenue stream is equal to the present value of the cost stream.
It should be compared with the financial opportunity cost of capital (FOCK),
or the weighted average cost of capital (WACC), to assess the financial
sustainability of a project.
Financial oppor tunity cost of capital. The opportunity cost of using
investment resources at market prices in a project. This is often taken as the
weighted average borrowing rate of capital used in the project
Financial sustainability
sustainability.. The assessment that a project will have
sufficient funds to meet all its resource and financing obligations, whether
these funds come from user charges or budget sources; will provide sufficient
incentive to maintain the participation of all project participants; and will be
able to respond to adverse changes in financial conditions.
Money-metric measure. The money-metric measure of welfare change
forms a fundamental building block in applied welfare economics. While an
individual’s welfare change is derived from physical consumption of goods
and services, it is necessary to make accounting by putting some moneymetric units to the welfare change for any policy application. In a project
context, economic prices of commodities, which deviate from observed market
prices, represent the money-metric measure of welfare change when additional
unit of project input or output is added or taken away from the economy. In
a context of policy changes, there are other money-metric measures of welfare
changes such as compensating variation (CV) and equivalent variation (EV).
For more details, readers are referred to any graduate level microeconomic
textbooks.
Net present value (NPV). The difference between the present value
of the benefit stream and the present value of the cost stream for a project.
The net present value calculated at ADB’s discount rate should be greater
than zero for a project to be acceptable.
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
Numeraire. The common yardstick that measures the objective being
maximized. In project financial analysis this yardstick is the real income
change for the project participants valued in domestic market prices. In
project economic analysis, because the scope of the analysis differs, and
because domestic market prices do not always reflect the scarcity value of
project outputs and inputs, this yardstick is the real change in net national
income for the project as a whole valued in economic prices. Generally, the
real change in net national income can be measured at two different price
levels. These are the domestic price level, where all economic prices are
expressed in their equivalent domestic market price level values (the domestic
price numeraire), and the world price level, where all economic prices are
expressed at their equivalent world market price level values (the world
price numeraire). As long as consistency is maintained in a particular
calculation across all project effects, project decisions will not be affected
by whether the domestic price level or the world price level is used to express
the numeraire.
Oppor tunity cost. The benefit foregone from not using a good or
resource in its best alternative use. Opportunity cost measured at economic
prices is the appropriate value to use in project economic analysis for valuing
nonincremental outputs and incremental inputs. For details, see ADB
Guidelines for the Economic Analysis of Projects (1997).
Pover
ty impact ratio (PIR). The proportion of the total net benefits of
overty
a project (NPV) that accrues to the poor. (It is possible that its value exceeds
one as in Case 2 in Appendix 6.)
Present value (PV). The value at present of an amount to be received
or paid at some time in the future. It is determined by multiplying the future
amount by a discount factor.
Real exchange rate. The price of foreign currency in terms of domestic
currency where the rate of exchange is adjusted for the relative value of
actual or expected domestic and international inflation.
Risk analysis. The analysis of project risks associated with the value
of key project variables, and therefore the risk associated with the overall
project result. Quantitative risk analysis considers the range of possible
values for key variables, and the probability with which they may occur.
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G LOSSARY
149
Simultaneous and random variation within these ranges leads to a combined
probability that the project will be unacceptable. When deciding on a
particular project or a portfolio of projects, decision makers may take into
account not only the expected scale of project net benefits but also the risk
that they will not be achieved.
Sensitivity analysis. The analysis of the possible effects of adverse
changes on a project. Values of key variables are changed one at a time, or
in combinations, to assess the extent to which the overall project result,
measured by the economic net present value, would be affected. Where the
project is shown to be sensitive to the value of a variable that is uncertain,
that is, where relatively small and likely changes in a variable affect the overall
project result, mitigating actions at the project, sector, or national level should
be considered, or a pilot project implemented.
Shadow exchange rate (SER). The economic price of foreign currency
used in the economic valuation of goods and services. The shadow exchange
rate can be calculated as the weighted average of the demand price and the
supply price for foreign exchange. Alternatively, it can be estimated as the
ratio of the value of all goods in an economy at domestic market prices to
the value of all goods in an economy at their border price equivalent values.
Generally the shadow exchange rate is greater than the official exchange
rate, indicating that domestic purchasers place a higher value on foreign
currency resources than is given by the official exchange rate.
Shadow exchange rate factor (SERF). The ratio of the economic price
of foreign currency to its market price. Alternatively, the ratio of the shadow
to the official exchange rate. This factor will generally be greater than 1. For
economic analysis using the domestic price numeraire, the SERF is applied
to all outputs and inputs, including labor and land, that have been valued at
border price equivalent values, with project effects measured at domestic
market price values left unadjusted. The inverse of the standard conversion
factor.
Shadow wage rate (SWR). The economic price of labor measured in
the appropriate numeraire as the weighted average of its demand and supply
price. For labor that is scarce, the SWR is likely to be equal to or greater
than the project wage. For labor that is not scarce, the SWR is likely to be
less than the project wage. Where labor markets for labor that is not scarce
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H ANDBOOK FOR I NTEGRATING P OVERTY I MPACT A SSESSMENT IN THE E CONOMIC A NALYSIS OF P ROJECTS
are competitive, the SWR can be approximated by a market wage rate for
casual unskilled labor in the relevant location, and adjusted to the appropriate
numeraire.
Shadow wage rate factor (SWRF). The ratio of the shadow wage rate
of a unit of a certain type of labor, measured in the appropriate numeraire,
and the project wage for the same category of labor. Alternatively, the ratio
of the economic and financial cost of labor. The SWRF can be used to convert
the financial cost of labor into its economic cost.
Standard conversion factor (SCF). The ratio of the economic price value
of all goods in an economy at their border price equivalent values to their
domestic market price value. It represents the extent to which border price
equivalent values, in general, are lower than domestic market price values.
The SCF will generally be less than one. For economic analysis using the
world price numeraire, it is applied to all project items valued at their domestic
market price values to convert them to a border price equivalent value, while
items valued at their border price equivalent value are left unadjusted. The
SCF and SERF are the inverse of each other.
Transfer payment. A payment made without receiving any good or
service in return. Transfer payments transfer command over resources from
one party to another without reducing or increasing the amount of resources
available as a whole. Taxes, duties, and subsidies are examples of items that,
in some circumstances, may be considered to be transfer payments.
Weighted average cost of capital (W
ACC). Measured on after-tax
(WA
income tax basis, WACC is determined by ascertaining the actual lending (or
onlending) rates, together with the cost of equity contributed as a result of
the project. To obtain the WACC in real terms, the inflation factor is to be
deducted from the estimated cost of borrowing and equity capital.
Willingness to pay (WTP). The maximum amount consumers are
prepared to pay for a good or service. WTP can be estimated as the total area
under a demand curve. Changes in WTP can occur when the demand curve
itself shifts because of changes in income or in the prices of substitute
goods.
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References
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