A User's Guide to Poverty and Social Impact Analysis

A User's Guide to Poverty and Social Impact Analysis
A User’s Guide to
Poverty and Social Impact Analysis
The World Bank
Poverty Reduction Group (PRMPR) and
Social Development Department (SDV)
© 2003 The International Bank for Reconstruction and Development/The World Bank
1818 H Street, N.W.
Washington, D.C. 20433, USA
The findings, interpretations and conclusions expressed in this document are entirely those of the authors, and
should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its
Board of Executive Directors or the countries they represent. For electronic copies of this document in English,
French, Russian and Spanish, please visit the PSIA website at www.worldbank.org/psia.
Contents
Acknowledgments
v
Acronyms
vi
Purpose of the User’s Guide
vii
1
Introduction
1
2
A Conceptual Framework for Understanding Poverty and Social Impacts
3
Impact of what: What is being analyzed?
Impact on what: What is the welfare measure being assessed?
Impact on whom: Whose welfare is being analyzed?
Impact how: How are impacts channeled?
Impact how: How do institutions affect outcomes?
Impact when: When do impacts materialize?
Impact if: What are the risks of an unexpected outcome?
3
3
4
4
6
6
7
Elements of Good Poverty and Social Impact Analysis
9
3
4
Element 1: Asking the right questions
Element 2: Identifying stakeholders
Element 3: Understanding transmission channels
Element 4: Assessing institutions
Element 5: Gathering data and information
Element 6: Analyzing impacts
Element 7: Contemplating enhancement and compensation measures
Element 8: Assessing risks
Element 9: Monitoring and evaluating impacts
Element 10: Fostering policy debate and feeding back into policy choice
9
10
12
13
14
18
27
29
31
34
Challenges and Operational Principles
39
Constraints
Principles
39
40
5 Possible Summary Matrix
42
6 Conclusions
45
iii
A User’s Guide to Poverty and Social Impact Analysis
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
47
Bibliography
81
Boxes
1.
2.
3.
4.
5.
6.
7.
8:
9.
10.
11.
12.
13.
14.
Asking the Right Questions
Analyzing the Impact of Mine Closure in Russia: Stakeholder Analysis
Interest Groups and Collective Action
Decentralization in Indonesia: Institutional Analysis and Social Accountability
Illustrative Categorization of Selected Reforms according to Scale of Indirect Impacts
Impact of Public Expenditures in Indonesia: Average versus Marginal Benefit Incidence
Impact of Utility Pricing on the Poor in Armenia: Demand Analysis
Impact of Liberalization in Mexico: Supply-Side Analysis
Impact of Agricultural Subsidies and Tariffs in Turkey: Multimarket Modeling
Net Fiscal Incidence in the Philippines
Impact of the Indonesian Financial Crisis on the Poor: Partial Equilibrium Modeling and
CGE Modeling with Micro-simulation
Labor Downsizing and the Design of Compensation Packages in Vietnam
M&E Tools for Promoting Accountability and Transparency during Policy Reform
Poverty and Social Impact Analysis of Cotton Reform in Chad
10
11
12
14
20
23
24
25
26
27
Data Collection Methods
Considerations in Choosing Impact Analysis Approaches
Planning M&E as Part of Poverty and Social Impact Analysis
A Summary Matrix for Poverty and Social Impact Analysis of Reform
15
19
34
44
28
29
33
43
Tables
1.
2.
3.
4.
iv
Acknowledgments
website since April 2001. We would like to acknowledge the extensive comments received, among others,
from the ADB, Catholic Relief Services, Christian Aid,
DfID, GTZ, IMF, Ministrie van Buitenlandse Zaken
(The Netherlands), OXFAM, World Vision and World
Learning.
The team organized a wide range of consultations
during the two years from the initial outline to the
final version, including numerous meetings with nongovernmental organizations, as well as multilateral
and bilateral partner organizations. The approach was
refined through a series of learning events and a seminar series over these two years. The team also assisted
country teams to field test the core approach, reviewing the lessons learned during a workshop organized
jointly with the IMF and DfID in October 2002. The
discussions and comments from participants at those
events contributed to the development of the User’s
Guide.
Materials from this work have been posted on a new
website launched jointly by PRMPR and SDV
(www.worldbank.org/psia), which will continue to be
updated with new tools and methods, and country
applications produced by the World Bank, countries
and external partners. Suggestions and comments are
most welcome at [email protected]
This User’s Guide is a collaborative product of the
PREM Poverty Group (PRMPR) and ESSD’s Social
Development Department (SDV). It has been prepared by a team comprised of Jehan Arulpragasam,
Sabine Beddies, Sophie Brown, Aline Coudouel, Anis
Dani, Andreas Groetschel, Sarah Hague, Sarah Keener,
Timothy Kessler, Humberto Lopez, Mattias Lundberg,
Jonathan Maack, Nayantara Mukerji, Stefano Paternostro and Sharon White. Joyce Chinsen and Nelly
Obias provided technical support to PSIA team
through the entire production process. Cathy Sunshine helped to edit the final draft of the User’s Guide.
The User’s Guide team received useful advice from
John Page and Steen Jorgensen, and members of the
PREM Poverty and the Social Development Boards.
The report has also received substantive inputs from
the editors, Francois Bourguignon and Luiz Pereira da
Silva, and the authors of various chapters in the Toolkit
for Evaluating the Poverty and Distributional Impact of
Economic Policies. Ulrich Zachau and Stefan Koeberle
provided valuable advice, particularly on the interface
of this analytical approach with emerging trends
within the World Bank on policy-based lending.
Many others inside and outside the World Bank
provided valuable comments and feedback on the first
draft, which has been posted on the Bank’s external
v
Acronyms
ADB
Asian Development Bank
BA
Beneficiary assessment
CGE
Computable general equilibrium
DFID
Department for International Development (UK)
GTZ
Deutsche Gesellschaft für Technische Zusammenarbeit (Germany)
IMF
International Monetary Fund
IO
Input-output
M&E
Monitoring and evaluation
NGO
Nongovernmental organization
PETS
Public expenditure tracking survey
PPA
Participatory poverty assessment
PREM
Poverty Reduction and Economic Management Network (World Bank Group)
PRS
Poverty reduction strategy
PRSP
Poverty Reduction Strategy Paper
PSIA
Poverty and social impact analysis
QSDS
Quantitative service delivery survey
SAM
Social accounting matrices
SDV
Social Development Department (World Bank Group)
SIA
Social impact assessment
SOCAT
Social capital assessment tool
vi
Purpose of the User’s Guide
and techniques, through the Toolkit for Evaluating
the Poverty and Distributional Impact of Economic
Policies and the Social Analysis Sourcebook, available
on the World Bank website. Additional guidance on
economic and social analysis tools and methods is
under preparation. The Bank is also developing
guidance on issues, challenges, and tools that may be
of particular relevance in analyzing specific reforms.
A summary matrix and reform-specific notes will be
posted on an ongoing basis on the PSIA website.
More generally, the PSIA website presents resources
on economic and social tools and methods for PSIA,
country experience in undertaking PSIA for specific
reforms, training events and material, and other
resources: http://www.worldbank.org/psia. Suggestions and comments are most welcome
([email protected]).
Poverty and social impact analysis (PSIA) involves the
analysis of the distributional impact of policy reforms
on the well-being of different stakeholder groups, with
a particular focus on the poor and vulnerable. PSIA is
a systematic analytic approach, not a separate product.
This User’s Guide introduces the main concepts
underlying PSIA, presents key elements of good practice
approaches to PSIA, and highlights some of the main constraints and operational principles for PSIA. It is intended
for practitioners undertaking PSIA in developing countries. It does not set out operational policy or guidance to
World Bank staff. This User’s Guide highlights some of
the key tools that practitioners may find useful to analyze
poverty and social impacts of policy reforms, but does not
aim to be comprehensive in its coverage.
As a complement to this User’s Guide, the World
Bank has also developed guidance on selected tools
vii
1 Introduction
pace/sequencing or institutional arrangements of the
reform, or the introduction or strengthening of mitigation measures. Finally, ex-post PSIA assesses the
actual distributional impacts of a completed reform,
which helps analysts understand the likely impacts of
future reforms.
PSIA is not new, and lessons can be drawn from
past experiences.3 Effective PSIA is undertaken early
enough to inform the design of reforms, clearly sets
out the assumptions behind the analysis, addresses the
risks to policy implementation, considers all stakeholders in the analysis, and promotes transparency
about expected impacts to strengthen local ownership.
Analysts have typically faced constraints in terms of
data, analysis, capacity, and time. Some of these constraints can be addressed by building on earlier experience and by employing flexibility in the choice of
tools and methods.
The User’s Guide is organized as follows. Chapter 2
introduces the main concepts underlying PSIA and
establishes the conceptual framework. Chapter 3 then
presents an approach to PSIA by reviewing 10 basic
elements underlying the sound analysis of the poverty
and social impacts of reforms. Chapter 4 considers
some of the major constraints often identified by PSIA
practitioners, especially in developing countries, and
provides basic operational principles for PSIA. Chapter 5 proposes a summary matrix that can be a useful
tool to capture and integrate the various elements of
good PSIA. Finally, chapter 6 closes with brief conclusions.
Poverty and social impact analysis (PSIA) refers to the
analysis of the distributional impact of policy reforms
on the well-being or welfare of different stakeholder
groups, with particular focus on the poor and vulnerable.1 The adoption of the Poverty Reduction Strategy
Paper approach and of the Millennium Development
Goals has led to an increased need for more systematic
analysis of the poverty and social implications of
reforms. This User’s Guide is part of a comprehensive
response undertaken by the World Bank to address
those concerns.2
The User’s Guide is intended for practitioners
undertaking PSIA in developing countries. Given the
broad scope of policy issues, methods, and challenges
involved, the User’s Guide does not specify minimum
standards for PSIA, but rather provides suggestions on
how to approach the analysis. In advocating a multidisciplinary approach to PSIA, the User’s Guide presents both economic and social analysis tools and
methods. While focusing on distributional impacts,
PSIA also addresses issues of sustainability and risks to
policy reform that come with the poverty and social
impacts of policy changes.
PSIA includes ex-ante analysis of the likely impacts
of specific reforms, analysis during reform implementation, and ex-post analysis of completed reforms.
Each of these has a specific utility. Ex-ante PSIA can
inform the choice, design, and sequencing of alternative policy options. During implementation, the monitoring of a reform and its impacts can lead to
refinement of the reform, a reconsideration of the
1
A User’s Guide to Poverty and Social Impact Analysis
Notes
1. This User’s Guide uses the terms “well-being”
and “welfare” synonymously.
2. The PSIA website (http://www.worldbank.org/psia)
presents guidance on the application of economic and
social tools and methods for PSIA, country experience in
undertaking PSIA for specific reforms, training events
and material, and other resources.
3. The Bank has been engaged in this area for some
time, especially in the context of projects. For economic literature on the topic, see, among others,
Squire and van der Tak 1975; Timmer, Falcon, and
Pearson 1983; and Gittinger 1985. For anthropological
and sociological literature, see Finsterbusch, Ingersoll
and Llewellyn 1990; Becker 1997; Goldman 2000; and
Brinkerhoff and Crosby 2002.
2
2 A Conceptual Framework for Understanding
Poverty and Social Impacts
This chapter presents the main concepts underlying
poverty and social impact analysis. It addresses seven
key areas:
Tools for PSIA therefore must be able to address not
just major macroeconomic reforms, but also the key
structural and sectoral policy changes with which
countries are currently contending.2
This shift from broad-based “stabilization and
adjustment” suggests that PSIA should be undertaken
on a reform-specific basis. Such an approach also
makes the task of analyzing the impact of several
reforms more manageable. While it would be conceptually preferable to assess the combined effect of a
series of policy changes in a single analytical framework, few tools can accomplish this—and those that
can tend to be complex and data-intensive. Therefore,
it is often more practical to disaggregate expected
overall impacts to individual reforms, and consider
sequencing on a reform-specific basis. Consideration
of the impacts of a “package” of reforms is still pertinent, however. Where they cannot be analyzed in a single analytical framework, their combined effects on
various groups such as the poor may be most practically considered by independently assessing the impact
of each reform set on each group. However, such an
approach will tend to lose interaction effects.
■ What is being analyzed?
■ What is the welfare measure being assessed?
■ Whose welfare is being analyzed?
■ How are impacts channeled?
■ How do institutions affect outcomes?
■ When do impacts materialize?
■ What are the risks of an unexpected outcome?
Impact of what:
What is being analyzed?
Poverty and social impact analysis focuses on the
impact of policy change. The scope of the policy
debate in the development arena has now broadened
beyond macroeconomic stabilization and associated
measures to also include specific structural and public
expenditure reforms. This broader view is also implicit
in the poverty strategies of developing countries. In
fact, a review of fifteen Poverty Reduction Strategy
Papers (PRSP) shows that poverty strategies commonly focus on enhanced expenditure programs
(especially in health, education, water and sanitation,
and roads and infrastructure); institutional reforms to
improve governance (such as decentralization, civil
service reform, and tax reform); and structural
reforms (including trade reform, privatization, financial sector reform, and agriculture sector reform).1
Impact on what: What is the welfare
measure being assessed?
PSIA focuses on assessing distributional impacts on
welfare, or well-being, including both its income and
non-income dimensions. With poverty now recog-
3
A User’s Guide to Poverty and Social Impact Analysis
tion on protected commodity and labor markets can
derail trade liberalization. Similarly, vested interests
within the public sector can also derail reforms. PSIA
thus should identify and analyze the impact of policy
on other stakeholders, beyond the poor, who are
affected by or can influence reforms.
nized as multidimensional (World Bank 2000a), development efforts are being targeted to address both
income and non-income measures of welfare and
poverty, recently captured in part by the Millennium
Development Goals. Until recently, the income dimension of welfare was the main focus of poverty and distributional analysis, and economic tools were most
often applied in analyzing the money-metric welfare
measure.3 Now, however, non-income dimensions of
welfare and poverty—such as human development
and social development indicators addressing risk,
vulnerability, and social capital4—are being given
closer consideration. In undertaking PSIA, the analyst
will need to choose appropriate indicators of welfare
and poverty based on the country and policy context.
Impact how: How are impacts
channeled?
Policy reforms can be expected to have an impact on
various stakeholders through five main transmission
channels outlined below: employment; prices (production, consumption, and wages); access to goods
and services; assets; and transfers and taxes. Each policy reform is likely to have impacts through more than
one channel. For example, utility reforms might result
in changes in prices and access, but might also have an
impact on the fiscal stance of a country, and hence on
transfers and taxes. Further, different stakeholders are
likely to be affected differently through these channels.
For example, relative price changes will affect net consumers and net producers differently, and even among
these groups the impact may vary. For example, consumers will be affected differently depending on their
consumption patterns or their ability to substitute
goods.
Impact on whom: Whose welfare is
being analyzed?
PSIA is concerned with the distributional impacts of
policy change on various groups, with a particular
focus on the welfare of the poor and those vulnerable
to impoverishment. Depending on country circumstance, groups may be defined in terms of income
class, gender, ethnicity, age, geographic location, livelihood, or other such criteria. In practice, however,
household members do not always pool resources or
allocate benefits equally. When the impacts on different members within a household are likely to differ, it
is important to also analyze intra-household effects.
PSIA is concerned with distributional impacts for
two reasons. First, policy change can have a direct
impact on the welfare of the poor or other disadvantaged groups. Understanding the impacts of policy
change on these groups can inform the design of policy. Second, the distributional impacts of a policy, even
among non-disadvantaged groups, are important for
the effectiveness of that policy and its ultimate sustainability. Even if a policy change results in overall
welfare gains, it is likely that some groups may experience losses, at least in the short run. While losers may
not necessarily be poor, reduction in their welfare may
not be acceptable for social or political economy reasons and may significantly affect the implementation
and sustainability of the reform. For instance, business
and union interests that fear the impact of competi-
Employment
The principal source of income for most households is
employment. To the extent that a policy change affects
the structure of the labor market or the demand for
labor, particularly in sectors that employ the poor
(such as unskilled, rural off-farm, and agricultural
labor), the welfare of low-income households will be
affected. There may be direct transmissions through
this channel in the case of certain policies: for example, restructuring of a state enterprise may lead
directly to retrenchment of workers. In other cases
transmission may be indirect. For instance, macro
policies may stimulate faster growth, leading in turn to
increased employment among the poor; an exchange
rate depreciation or trade liberalization may result in
contractions and layoffs in the nontradable sector.
Alternatively, some policies will have different impacts
on formal labor markets and informal labor markets
4
A Conceptual Framework for Understanding Poverty and Social Impacts
electricity grid, particularly among the poor, can also
represent a welfare gain.5 In this regard, privatization
of service provision could either increase or decrease
access relative to public sector provision.6 Lack of
access to key infrastructure or services, either because
they do not exist or because they are of poor quality,
can limit the intended benefit of a policy. For example,
restructuring a marketing board may be fiscally desirable but may eliminate key market services where
alternatives do not exist. Structural or cultural norms
(such as restrictions on female mobility or female
property rights) may also impose higher transaction
costs or create barriers to access.
that employ many of the poor. For example, expenditure increase, reduction or switching may have different impacts on formal sector employment and
informal sector employment due to labor market segmentation (Agénor and Aizenman 1999).
Prices (production, consumption, and wages)
Prices determine real household income. Prices in the
markets for goods and services differentially affect the
real income of households to the extent that they consume or produce these products. How policy affects
prices will have an important bearing on income and,
directly or indirectly, on non-income measures of welfare. For all households, but especially for small farmers and the self-employed, price changes will affect
both consumption and resource allocation decisions.
On the consumption side, policies that cause an
increase in the prices of goods consumed by the poor
will have a direct negative effect on household welfare.
These can include import tariffs on traded staples, or
increased utility tariff rates. Consumer prices may be
indirectly affected as well, for example through expansionary monetary policy that leads to general price
inflation. Producers will also be affected by policies
that cause relative price changes—particularly changes
to the prices of their outputs or inputs. Producer
incomes are further affected by the difference between
farmgate and market prices, often conditioned by
transport costs and the degree to which private markets are efficient and competitive, rather than monopsonistic. Wage changes will affect net buyers and sellers
of labor differently, and policies that change relative
prices will induce shifts in both demand and supply.
Assets
Changes in the value of households’ assets will affect
income and non-income dimensions of welfare.
Changes in asset values can be due to changes in their
levels or their returns. Assets themselves can be categorized into five classes, all of relevance to poor households: physical (such as housing); natural (such as
land, water), human (such as education, skills); financial (such as a savings account); and social (such as
membership in social networks that increase access to
information or resources). Policy changes can have a
direct or indirect impact on these assets and their
returns. For example, land reform may directly result
in an increase or decrease in land assets of the poor.
Policy changes can also impact assets through indirect
channels. For example, inflationary policies will have a
negative wealth effect on those with monetary savings,
while participatory budgeting or community programs may increase social capital. Pricing or trade
changes could affect the natural resource assets of
households or groups (such as by increasing or
decreasing deforestation or desertification) or even
their human capital (such as by causing a deterioration in health conditions due to increased indoor air
pollution as a result of energy price changes). In many
cases, certain assets are also prerequisites to benefit
from a reform. For example, if farmers cannot reach a
market due to lack of transport, the benefits of price
liberalization are likely to be realized primarily by
middlemen and traders. It is important to take into
account the legal and regulatory frameworks when
analyzing this transmission channel; for example,
Access
Well-being will be affected by access to goods and services, whether through access to markets and service
outlets or through improvements in the quality and
responsiveness of public or private service providers.
Policy can affect access directly by enhancing provision of the infrastructure or services in question, or
indirectly by removing constraints to access by particular households or groups. For example, improved
road infrastructure could dramatically enhance access
to markets and services for groups in certain geographic areas. A policy that expands connections to an
5
A User’s Guide to Poverty and Social Impact Analysis
reform can affect institutions by changing organizational structures, roles, and responsibilities, or rules
and incentives, as well as by altering market incentives—for example, by removing price distortions or
encouraging competition. These reforms in turn affect
the behavior of economic agents and interest groups,
and thereby economic outcomes, including distribution and poverty reduction.
Many reforms depend for their implementation on
institutional change. This may involve creating new
organizations or changing rules and incentives to
achieve new objectives through existing organizations
(for example, improved cooperation among government agencies). Creation or modification of organizational structures does not in itself guarantee the
institutional changes necessary for the reform to succeed.8 Changes in formal rules of the game often must
be accompanied by changes in incentives in order to
alter the behavior of agents. Moreover, it is often
assumed that institutions (including markets) function smoothly and according to formal rules. In practice, high transaction costs, ineffective enforcement, or
lack of competition or accountability can lead to suboptimal performance of government, market, or civil
institutions. In some cases, institutional change
accompanying policy reform is not internalized by key
implementing agents, and the behavior of these agents
can thus lead to perverse policy outcomes.
Understanding the impact of a policy reform
requires an appreciation of the country’s organizational structures and the institutional rules governing
them. PSIA therefore depends upon careful organizational and institutional analysis of the formal and
informal rules, the behaviors of key stakeholders who
can affect reform outcomes, and the underlying
dynamics among them. This allows policymakers to
determine whether and how the existing rules and
informal practices will affect real costs and quality of
goods and services for the poor and other stakeholders
in the context of a specific policy change.
there are sometimes constraints on female land ownership.
Transfers and taxes
Household welfare, finally, is affected by transfers to
and from the household. These transfers can take the
form of private flows (such as gifts and remittances) or
public flows (such as subsidies and taxes). Public
finance has a direct impact on the welfare of specific
groups through transfers and tax policy. Public expenditure programs may focus on granting additional
resources to particular groups through transfer policies, which may be in the form of subsidies or direct,
targeted income transfer programs. Social protection
programs may be useful in protecting the poor against
risk and vulnerability, depending on their targeting.
Tax policy has direct distributional effects to the extent
that the resources or income of a household are taxed.
Regressive tax regimes disproportionately burden less
well-off households. Subsidies may be captured by the
non-poor or may simply be badly targeted. There may
also be a conflict between strict progressivity and the
political feasibility of policies (see Gelbach and Pritchett 2000). Poorer households may also be hurt in the
long run if the funds for public expenditure are borrowed and must be repaid; they will suffer either from
any attempt to “inflate away” the debt or from
increased future taxes needed for repayment. In societies with high gender inequities, the intra-household
impact of transfers may warrant special attention.
Impact how: How do institutions affect
outcomes?
The impacts of policy reform on economic agents are
mediated through institutions. Institutions are the formal and informal rules of the game in society; they are
the shared understandings that allow organizations to
interact.7 The impact of a policy reform is influenced
by the behavior of organizations. Organizations, in
turn, act in response to incentives created by the set of
public, private, and civil society institutions whose
rules mediate economic activity in the society (Rutherford 1994). These institutions include markets, legal
systems, and the formal rules and informal behavior of
implementing agencies, including government. Policy
Impact when: When do impacts
materialize?
A major challenge to PSIA is understanding that policies can affect different groups in very different ways.
6
A Conceptual Framework for Understanding Poverty and Social Impacts
tions for the success of the policy reform.9 Assumptions must be made explicit as to how economic agents
and institutions are expected to act (for example, the
sign and magnitude of an elasticity) and how policy
impacts would be transmitted to households. A second
set of assumptions concerns conditions exogenous to
the policy that need to be in place for the reform to
achieve its intended impacts. In addition to questions
of direct relevance to a reform, risks from underlying
country conditions (for example, ethnic tensions)
need to be factored into the risk assessment. Clearly
identifying and articulating critical assumptions will
serve to sharpen the rigor of the analysis, increase its
transparency, and facilitate its validation (and if necessary, correction) by knowledgeable stakeholders. The
analysis will also permit the monitoring of, and hence
improve the understanding of, transmission channels
and impacts, with possible adjustments to the reform
program over time.
This is in large part because some of the economic and
behavioral responses to a policy change can take time.
What is fixed in the short term may be variable in the
longer term. Understanding and explaining how
short-run losses may result in long-run gains for given
groups, or how immediate gains may lead to eventual
losses, is one of the challenges inherent to PSIA. For
instance, trade liberalization may cause employment
losses in the nontradable sector in the short term.
However, increased efficiency may later result in economic growth, and some of the laid-off workers may
find jobs in the expanding tradable sector. In addition,
some consumers may switch to cheaper nontraded
goods, thereby increasing consumption of these. The
combination of all these effects will determine the net
impact on different groups over the long term.
To take another example, a policy that pursues or
results in an overvalued exchange rate will benefit some
population groups in the short run (consumers and
importers). But if the overvaluation proves unsustainable in the long run and devaluation occurs, those same
groups will be negatively impacted. The net effect for
these groups of having an initially overvalued exchange
rate (a gain) followed by a devaluation (a loss) will
clearly depend on the magnitude of the deviations. The
international evidence suggests that sustained overvaluations may lead to abrupt currency collapses (as in
Mexico in 1994, East Asia in 1997, Brazil in 1999, and
Argentina in 2002) that are likely to generate net longrun losses. The issue becomes even more complex if
one considers the impact on exporters. Unlike
importers, exporters are harmed by an overvalued
exchange rate and are likely to benefit from devaluation. Eventually it will be necessary to consider both the
net effects across groups for a given time horizon and
the net effects over time for a given group.
Notes
1. Fifteen PRSPs were completed by end July 2002 and
included those for Albania, Bolivia, Burkina Faso,
Guyana, Honduras, Malawi, Mauritania, Mozambique,
Nicaragua, Niger, Tanzania, Uganda, Vietnam, Yemen,
and Zambia. Seven of these strategies call for utility
reforms; 5 for reforms of public sector pensions; 6 for
civil service reforms; 7 for fiscal decentralization; 11 for
reforms in the tax system (incl. VAT and other consumption taxes); 11 for land reforms; 10 for trade reforms; and
6 for reforms of the macro-economic framework.
2. Of course, structural changes could have macroeconomic effects. For instance, trade liberalization
could have serious consequences for the fiscal deficit,
the current account deficit, and macroeconomic stability. Understanding how these impacts affect the
poor is critical to PSIA.
3. This User’s Guide lays out existing economic and
social tools and approaches for distributional analysis
in order to give a broader picture of poverty to policy
analysts and decisionmakers. Insofar as the economic
tools draw on existing examples of such analysis, applications focus mainly on income/expenditure measures
of welfare. Increased attention to assessing the impacts
of policy on non-income measures of welfare is an
Impact if: What are the risks of an
unexpected outcome?
The design of reforms is based on underlying assumptions about the context and the behavioral response of
key institutional and human actors. If these assumptions are not realized, reform outcomes are at risk. A
crucial element of PSIA, therefore, is understanding
and (publicly) articulating ex ante the key assump7
A User’s Guide to Poverty and Social Impact Analysis
7. Organizations are purposive entities (such as
public agencies or firms) that have a formal structure
and seek to achieve certain objectives within the
opportunities and constraints afforded by the institutional framework of society (North 1990).
8. Formal changes in organizational structure are
relatively easy to make but may take much longer to be
institutionalized. In such cases, it is important to pay
attention to the capacity and accountability of the
concerned agencies as well as the power relations
within them. Understanding these issues allows for the
mobilization of existing capacity and for the tailoring
of interventions to the institutional and organizational
contexts in which they will be implemented.
9. Forecasting or simulating likely impacts of policy
by definition presupposes a view of likely causality and
behavior. Depending on the analyst’s information base
these can be empirically “estimated” based on the past,
derived on the basis of theory, or assessed on the basis
of knowledge of the country context and discussions
with key stakeholders and experts.
important priority for future work. The social development tools described in this User’s Guide are more
focused on non-income dimensions of poverty, such as
stakeholder interests, social capital, and vulnerability.
4. The World Bank’s Social Development Department has developed a new tool that provides data on
these indicators from Bank- and non-Bank sources for
country-level applications.
5. To the extent that increasing access is viewed as a
reduction in transport and transaction costs, it is
effectively reducing the “price” of the good or service
in question.
6. Sometimes an increase in access may come at the
cost of a higher price (or, where there was previously
no access at all, access may be granted at a price that is
prohibitive for the poor). In urban Peru, liberalization
of telephone services led to greater access for the poor
as well as lower prices. On the other hand, liberalization of electricity has led to greater access and reliability, but higher prices and lower overall consumption
(Torero and Pascó-Font 2001).
8
3 Elements of Good Poverty and Social Impact
Analysis
Element 1: Asking the right questions
Although there is no methodological template for analyzing the poverty and social impacts of policy, it is
possible to identify a number of elements that make
for good-practice PSIA. This chapter outlines 10 key
elements that those attempting to undertake or advise
on PSIA will need to address:
The first step in the analysis of poverty and social
impacts is to identify the reforms that will be subject
to analysis. This requires identification of the set of
reforms included in the government’s agenda that are
likely to have an impact on the distribution of income
or assets. Ideally, if time and resources permit, PSIA
should be carried out for each of these reforms. In
practice, analyzing all the reforms in a development
plan may not be realistic, so it will be necessary to further narrow down the reforms selected for analysis to
a manageable number. This selection process will
inevitably be a matter of judgment at the country
level, and will likely depend on factors such as:
1.
2.
3.
4.
5.
6.
7.
Asking the right questions
Identifying stakeholders
Understanding transmission channels
Assessing institutions
Gathering data and information
Analyzing impacts
Contemplating enhancement and compensation
measures
8. Assessing risks
9. Monitoring and evaluating impacts
10. Fostering policy debate and feeding back into policy choice.
■ The expected size and direction of the poverty and
social impacts
■ The prominence of the issue in the government’s
policy agenda
■ The timing and urgency of the underlying policy or
reform, and
■ The level of national debate surrounding the
reform.
While there is a logical sequence to these elements, this
does not imply that they need to be undertaken in strict
order or that all the steps will be feasible in every country.
This chapter provides a broad overview of specific methods and tools that can be used to address each of these
elements, pointing to the annex for further details and
references; those methods and tools discussed in the
annex are presented in bold in the text. (Building country
capacity is presented in chapter 4 as one of the overarching principles for operationalizing PSIA, rather than in
this chapter as a discrete element of PSIA.)
After selecting the reforms that will be subject to
PSIA, the second step is to formulate key questions for
analysis. This requires an understanding of the underlying problems that the reform is intended to address
(see box 1). A focus on overly narrow questions, or
exclusively on short-term effects, may obscure issues
9
A User’s Guide to Poverty and Social Impact Analysis
Element 2: Identifying stakeholders
that could prove critical to the achievement of a particular policy objective, or to informing policymakers
and stakeholders of the tradeoffs inherent in a certain
policy. A useful device is to conduct a problem diagnosis by organizing the chain of cause-effect relationships, from policy objectives and policy actions to
impacts, in the form of a hierarchical problem tree,1 in
order to formulate relevant research hypotheses.
Identifying policy constraints is a key component of
the analytical process and can often prevent subsequent missteps. Policy reforms are often implemented
to remove constraints that stand in the way of achieving certain development goals. For instance, a country
may be unable to balance its budget because of unsustainable losses by state-owned enterprises. The problem in this case will be to improve the overall fiscal
balance as well as the performance of individual agencies. For some objectives there may be multiple constraints, some being more important than others. In
such cases, it may be necessary to pursue more than
one policy reform, but also to be on the alert for interactive effects that those reforms might have on each
other. In another example, a policymaker faced with
inadequate public revenues may decide to raise taxes.
However, this will not be the appropriate response if
the real problem is that expenditures are too high,
rather than that revenues are too low. In order to avoid
inappropriate or mismatched policies, it is important
that the constraints on development objectives be
made explicit—rather than assumed—at the beginning of the PSIA process.
After asking the right questions and identifying the
problem that requires solution, an early identification
of relevant stakeholders is important. Not only can
policy choices affect different stakeholders or economic agents in different ways, but these stakeholders
can also influence whether a policy is adopted and
how it is implemented.
Stakeholder analysis identifies people, groups, and
organizations that are important to take into account
when conducting PSIA.2 It identifies and analyzes those
who are affected by the policy, as well as those who can
potentially affect policy implementation. Identifying and
disaggregating the stakeholders in the first category—
beneficiaries and those who suffer adverse impacts—is
central to the analysis of poverty and social impact of
policy. They can be disaggregated by a large number of
characteristics such as household type, household size,
ethnicity, gender, location, occupation, and so forth. For
modeling work, stakeholder analysis can serve as an
input into determining how best to disaggregate representative household groups or subgroups. Stakeholders
in the second category—organized groups such as
unions, business associations, donors, and civil society
organizations—may become sources of support or
opposition to policies. Analyzing such influential actors is
essential to understand behavioral responses that condition impacts, and the likelihood of reform success. Box 2
illustrates the use of stakeholder analysis to address the
impact of mine closures in Russia.
Box 1. Asking the Right Questions
between distribution and generation investments, were equally
important to achieving a more sustainable energy sector.
The analysis of a fiscal reform ideally includes an evaluation of
the short-term impact as well as the expected longer-term
impact and the assumptions underlying the realization of
long-term benefits. But beyond the dynamic impact of the
reform, the analyst should also consider whether structural
issues are affecting the country’s fiscal performance.
Reform of the sugar sector in Guyana is being analyzed
because of its fiscal cost and the number of people affected by
the reform. The analysis is comparing the reform’s direct
impacts on employment and indirect effects on municipal services and dependents with the long-term employment and fiscal losses that would likely occur if the sector were to continue
in its current state, given the continuing decline in world sugar
prices and the phasing out of preferential prices under the
Lomé Accord.
In PSIA work in the Pakistan energy sector, the initial focus
was on an electricity tariff increase to cover costs that represented a significant and chronic fiscal drain. Further problem
analysis revealed that questions about the increased costs of
power generation and non-tariff charges, and the imbalance
10
Elements of Good Poverty and Social Impact Analysis
Box 2. Analyzing the Impact of Mine Closure in Russia: Stakeholder Analysis
municipal and oblast-level governments were based partially on the revenues that each could muster in the event of
mine closure.
In the early 1990s, the Russian coal industry was in a state of
crisis. A large number of economically inefficient mines were
kept afloat by subsidies that reached $2.76 billion (more than
1 percent of GDP) in 1994. Restructuring entailed closing 183
loss-making mines and downsizing the workforce (including
those involved in coal production, administration, social services, and other auxiliary activities) from 900,000 in 1992 to
328,000 by end-2001.
The differences between stakeholder groups lay largely in their
analysis of the core problem. On the one hand, the Ministry of
Energy, regional governments, the labor union, and the mine
face workers advocated a narrow solution focused on preserving the mining industry in some form. On the other hand,
municipal governments, social service workers employed by
the mines, and local businesses focused on the need to find new
drivers of growth in mono-industry towns, as well as sources
of funding for services previously supplied by the mines.
Municipal governments did not have the revenue base to support the schools and other services formerly provided by the
mines, and were hard hit by the closures.
The Bank provided $1.3 billion in loans and played a major
role in helping the Russian government develop its strategy for
mitigation of the poverty and social impact of coal sector
restructuring. The team carried out a stakeholder analysis
using structured interviews in Moscow, mine visits, and discussions with union leaders. The analysis was designed to clarify the nature of the problem, identify the interests of various
actors, and develop a solution for effective fund transfer using
existing actors.
An Interagency Coal Commission with representatives from
municipalities, ministries, and government agencies helped
discuss and plan reforms. The Ministry of the Treasury was
identified as a transparent channel through which social protection funds could be transferred directly to the workers,
rather than moving funds through the Ministry of Energy and
regional governments. The analysis of stakeholder interests
was used to create a system of checks, balances, and independent assessments to ensure that all actors followed the rules laid
out in mine closure plans.
The team grouped the stakeholders into several categories.
Government ministries were not seen as neutral agents, and
their interests were explicitly identified. Similarly, the
options of mine employees were differentiated by their previous employment. Workers on the mine face, analytic and
administrative support workers, and workers in the schools
and hospitals previously funded by mine revenue would be
impacted differently as mines closed. The interests of
Sources: Lockhart 2001; Haney and others 2003.
social tensions, key informant interviews may be
needed to analyze the interests of stakeholders whose
support is critical to reform implementation, including those within government agencies, or interest
groups able to influence reform. Analyzing interests of
stakeholders who are less organized may involve special surveys or focus groups.
Stakeholder analysis contributes to an assessment
of the extent of country ownership of a particular policy in order to predict how different interests are likely
to influence government in general, and the policy
process in particular. Ownership assessment reveals
sources of potential resistance to policy change and
provides a rough estimate of the location and extent of
pressure that government will face in adopting a policy reform. This helps to assess government’s willingness to undertake and stick with the reform over time.
Weak ownership can lead governments to abandon
reforms midterm or produce distorted policies. For
A distinction should be made between stakeholders
who share multiple characteristics that enable them to
coalesce as a cohesive group (for example, labor
unions) and those that are analytical categories rather
than organic groups (for example, “the fourth income
quintile” or “the poor”). Stakeholder analysis goes
beyond simply identifying groups to analyzing the
stated or unstated interests of actors in relation to a
policy, as well as the nature and degree of their organization or ability to mobilize behind a common purpose (see box 3). To the extent that groups of the
second type are atomized or unorganized (such as
landless peasants, non-unionized workers, small businesses, consumers), they are less likely to be able to
easily voice their opposition to or support for a policy,
even if their support may be crucial to reform success.3
While secondary resources such as social science
research, news media reports, and advocacy literature
can help identify broad political economy issues and
11
A User’s Guide to Poverty and Social Impact Analysis
Box 3. Interest Groups and Collective Action
Estimating the influence of a particular group over decisions is
as much art as science. However, there are some useful criteria
for predicting the propensity of a group to lobby the government. The logic of collective action suggests that interests will
exert more pressure on policymakers or elected leaders when:
(a) the number of group members is small; (b) the benefits or
rents that would accrue to each member from the desired policy are very substantial and easy to perceive; and (c) members
have the means, especially the financial resources and networks, to protect their interests. The behavioral premise is simple: people fight harder when they have a large personal stake.
In contrast, the more diffused interests of unorganized groups
such as consumers are typically less influential. Many development interventions are designed to reduce or eliminate rents
among a small group of privileged interests and increase the
overall welfare of the public. These are precisely the policies
that are most likely to be fought, making either tough political
decisions or a concerted communications strategy paramount.
However, if the impact is sufficiently large, public interest
groups may emerge to advocate the interests of the less powerful, or violent street protests may break out. For example, the
Consumers Rights Commission of Pakistan was formed to
advocate consumer interests on tariff reform, and this lobbying is substituting for more violent forms of urban protest.
example, some countries pursue bank deregulation
and privatization, but refuse to remove barriers to
entry because of entrenched interests, resulting in an
oligopolistic sector that charges high interest rates and
provides poor services.
Factors that typically affect ownership can be analyzed by looking at both the political economy of a
country and its diversity (based on ethnic, religious,
linguistic, gender, and age differentials). By considering
political economy, analysts can identify affected groups
and assess their influence over government decisionmakers. Taking stock of diversity is important because
reforms may polarize existing tensions in the short
term, even while improving welfare in the long run.
sion channels that are going to dominate and require
analysis will vary and will have distinct impacts on different stakeholders, depending on the reform and the
country context. Impacts may differ along two key
dimensions: first, they can be direct or indirect, and
second, they can occur in the short or the long term.
Some policy reforms may have primarily direct
impacts, that is, impacts that result directly from
changes in the policy levers altered by the reform. For
example, an increase in the value-added tax will translate directly into lower purchasing power for a given
disposable income. Reforms may also have important
indirect impacts, that is, impacts resulting from the
reform through channels other than the actual policy
lever or action. Thus an increase in value-added tax
rates will have a positive impact on the fiscal stance of
the country; if this is translated into increased government expenditure, it will have impacts on various
groups of households through the goods, services,
transfers, and subsidies they receive. Such a stronger
fiscal stance also will likely generate improved growth,
affecting household welfare.
The second critical dimension relates to the timing
of impacts. Given that the nature of the impacts may
change over time, so will net impacts on various stakeholders. To keep our earlier example of an increase in
value-added tax rates, the direct impacts on purchasing
power will likely be felt in the very short term, while the
indirect impacts of improved service delivery and
higher growth will typically take more time to materialize. Stakeholders might therefore feel both negative
and positive impacts, but at different points in time.
Element 3: Understanding transmission
channels
Once potential stakeholders have been identified, an
important early step in the PSIA process is to delineate
the channels by which the analyst expects a particular
policy change to impact various stakeholder groups.4
It is important to explicitly present the hypotheses and
assumptions underlying this analysis. These can then
be tested empirically through economic and social
analysis techniques.
As discussed in chapter 2, the expected impact of a
policy change on the welfare of target groups and
other key stakeholders takes place through five main
transmission channels: employment, prices (production, consumption, and wages), access to goods and
services, assets, and transfers and taxes. The transmis12
Elements of Good Poverty and Social Impact Analysis
Element 4: Assessing institutions
also be useful to undertake concurrently an analysis of
the constraints to private sector entry and participation. Quantitative or qualitative household surveys
can also reveal who buys services, where, and at what
price. Quantitative service delivery surveys and citizen
report cards can be applied to the analysis of the effectiveness of state marketing agencies. Price analysis is
always useful in ascertaining the competitiveness of a
market and of market structure.
As discussed above, institutions affect the impact that
policies have on poverty and the welfare of different
households or groups. First, institutions mediate the
transmission of certain policy impacts to people. Understanding social and market institutions helps to understand impacts of a given policy change (such as
deregulation, privatization, or removal of an export tax).
Second, institutions are often the object of many types of
policy reform. Privatization, civil service reform, decentralization, and expenditure management reform are
examples of institutional reform that involve changes in
the incentives and rules that govern public and private
organizations. Third, many policy changes depend on
particular organizations for their implementation. The
incentives, performance, and capacity of these organizations will be critical to the actual implementation of the
policy and thus its impact. Fourth, aside from wellknown barriers to entry faced by the poor, institutionspecific intents of the reform may introduce new
transaction costs stemming from information asymmetry and bounded rationality that affect market behavior
or access to public services (Powers 2003). Two key areas
of focus for PSIA are the analysis of market structure
and the analysis of implementing agencies.
Analysis of implementing agencies
In judging the likely poverty impacts of reforms that
involve a change in government responsibility, or cooperation among government agencies or other implementing
agencies, the flow of decisionmaking, information, and
resources within and among organizations needs to be
considered (see box 4). Two options for collecting this
kind of information are organizational mapping and the
institutional assessment tool.
Organizational mapping is a method that enhances
understanding of the internal behavior of organizations by creating an inventory of the actors carrying
out reforms and explicitly revealing relationships
among them. Organizational mapping has two components: static mapping and process mapping. Static
mapping identifies ex ante the specific public actions
associated with a policy reform, and the organizations
(which may be outside government) responsible for
implementing them. It maps out the relations among
the implementing agencies and identifies those
expected to support or obstruct the reform. The exercise is informed by earlier stakeholder analysis (see the
section above on identifying stakeholders) of government and other organized actors. Process mapping
draws on work carried out to improve efficiency in the
public and private sectors in industrialized countries
(Hunt 1996). It identifies current practices and norms
in relevant organizations that cannot easily be gleaned
from documents or diagrams. It does so by tracing
flows of critical resources, decisionmaking authority,
and information in the current system. This helps create an understanding of the rules and incentives that
affect internal behavior and the extent to which organizations pursue development objectives. Process mapping can help identify constraints to effective policy
implementation at three levels: in organizational pro-
Analysis of market structure
Surveys among consumers and producers of goods
and services can be useful approaches to enhancing
understanding of context-specific market structure.
Identifying the nature of the market (monopoly,
monopsony, oligopoly, perfectly competitive, etc.) and
what determines this market structure (natural
monopoly, restrictions to entry, or collusion, for
example) is a crucial first step toward understanding
the enabling conditions that would need to be created
for market reform to lead to improvements in performance and better outcomes for the poor.
Enterprise (or trader) surveys can be useful for
understanding the nature of the market, the number
and types of economic agents, and market constraints,
as well as de jure and de facto barriers to entry and
transaction costs. In the case of privatization or liberalization, where an assumption is that market entry
will lead to competition and price reduction, it might
13
A User’s Guide to Poverty and Social Impact Analysis
Box 4. Decentralization in Indonesia: Institutional Analysis and Social Accountability
lage level. An existing government agency, the Department of
Community Development, acted as a partner and enforcing
agency.
A research team led by Scott Guggenheim carried out an
institutional analysis of village-level governmental structures
and traditional village decisionmaking bodies in Indonesia as
part of a decentralization project designed to address corruption and top-down decisionmaking. The Kecamatan Development Project (KDP) was committed to using local capacity
rather than developing a separate project implementation
unit. The analysis, conducted through focus groups and
interviews with government officials, helped to identify the
relative strength and capacity of existing systems, the flow of
money and information, and the location and nature of decisionmaking in the chain. The project changed the role and
authority of those structures, shifting the locus of power
within the system from regional governmental bodies to village councils. Through the interview process, the team identified the Village Infrastructure Project as a field-tested
means to get money directly from central accounts to the vil-
The KDP used transparency and social accountability to make
the new institutional structure work. Existing village councils
at the kecamatan (subdistrict) level, which were formal organizations that had met once a year to feed into the government’s
planning process, became the primary decisionmaking bodies.
Decisions on proposals from villages were made in public
meetings of the council, procurement forms were limited to
one page, expenditure information was kept on cash ledgers,
and information about the program was disseminated through
posters, flyers, and radio broadcasts. Further, the KDP worked
with the Association of Independent Journalists to ensure
media coverage and gave small grants to reporters to build
capacity for independent reporting.
Source: National Management Consultants 2000.
(a) roles; (b) knowledge and access to information; (c)
incentive structures; (d) receptivity to policy change;
(e) capacity; (f) resources or financial clout; and (g)
scope to adapt to the new reform agenda. The advantage of the institutional assessment tool is that it can
enable more systematic analysis of issues ranging from
political incentives to administrative capacity at low
cost. The disadvantage is that the tool relies on a desk
assessment, and lacks the interactive dimension of
interviews with staff of the organizations that are being
reformed. The tool is currently better suited for the
analysis of institutions with respect to investment
operations, but it could be used to assess institutions in
the context of the implementation of policy reform.6
cedures, in the relationship between organizations,
and in the relationship with the authorizing environment. Addressing them may require fine-tuning procedures, recasting fundamental rules of operation, or
even replacing entire organizations. Process maps are
constructed through in-depth, semi-structured interviews with staff at all levels of the organization, focusing particularly on those at the “front line” of
delivering services. The main advantage of organizational mapping is its ability to expose a problem area
that may not be readily seen by relying directly on
stakeholders to describe their interests and constraints
(see box 4). A drawback is that it is more time-consuming, costly, and technically demanding than
guided questionnaires. Good process mapping needs
to be used iteratively to test assumptions by monitoring institutional performance over time.
The institutional assessment tool was designed to permit an institutional analysis of various components of
a project. The tool consists of questions that help the
analyst structure thinking about the complex relationships and processes within organizations upon which
reforms depend.5 The questions are used to evaluate
the effectiveness of institutions, from performance
incentives to their capacity to implement policy. They
address key issues of relevant organizations, including:
Element 5: Gathering data and
information
Assessing data needs and available data and planning
the phasing of future data collection efforts are an
important part of PSIA. Identification of data needs
will benefit from the prior identification of policy
issues, stakeholders, and likely transmission channels,
as outlined above. Four discrete steps are suggested:
mapping out desirable data for PSIA; taking stock of
available data and analysis; coping with PSIA data lim14
Elements of Good Poverty and Social Impact Analysis
The approach based on quantitative analysis, numeric
data, and close-ended data collection offers certain
advantages. Analyzing the poverty and distributional
impacts of policy on welfare indicators will require linking data at the macro or sectoral level (generally corresponding to the level of policy intervention) to
disaggregated household-level data that capture the welfare measure of interest (usually an income/expenditure
aggregate, but possibly other welfare measures such as literacy or infant mortality) and other behavioral variables
(such as access). Close-ended surveys have generally been
used to collect such data. For analysis to be generalizable,
data should be derived from a random sample. When the
reform is expected to impact only a discrete group (for
example, laid-off mine workers) or a geographic subregion, purposive sampling of just that group or subregion
may be more appropriate and economical than a nationally representative survey. Numeric data can be used to
undertake statistical and multivariate analysis to test
hypotheses and determine relationships (see table 1).
itations up front; and addressing PSIA data limitations
today so they do not limit PSIA in the future.
Mapping desirable data for PSIA
Analysis of the poverty and social impacts of policy
can be extremely data-intensive. Specific data requirements will, of course, depend on the nature of the
reform being analyzed and the analytical tool or technique being employed. In approaching data and methods, it is useful to distinguish among data collection
instruments (close-ended or open-ended); data type
(numeric or non-numeric); and associated methods of
data analysis (quantitative or qualitative). Traditionally, analytical approaches have been either quantitative in nature and based on numeric data collected
using close-ended data collection methods, or qualitative in nature and based largely on non-numeric data
collected using open-ended data collection methods.
“Mixed methods” are increasingly being employed and
are extremely useful for PSIA.
Table 1. Data Collection Methods
Aspect
Close-ended
Open-ended
Data collection
instrument
• Structured, formal, predesigned
questionnaires, such as living standards
measurement study, social impact assessment surveya, willingness-to-pay survey,
client satisfaction survey, citizen report card.
• In-depth, open-ended, or semi-structured interviews, such as key informant interviews and case
histories, focus group interviews, community interviews, mini-surveys.
• Ethnographic observation.
• Systematic (or directed) consultation, such as beneficiary assessment.
• Participatory data collection methods, such as participatory action research, participatory rural
appraisal, participatory public expenditure review.
• Focus group discussion.
• Community and institutional surveys.
• Written documents (for example, program records, process documentation, media reports).
• Participatory visual exercises.
Analytic method
• Predominantly statistical analysis.
• Deductive reasoning.
• Inductive reasoning.
• Interactive analytical process: research questions formulated, answered, and analyzed iteratively,
e.g. in stakeholder analysis, participatory poverty assessment, scenario analysis.
• Methods tailored to social context.
Advantages
• Findings can be generalized.
• Can quantitatively estimate size and
distribution of impacts.
• Explains statistical correlations.
• Able to analyze behavioral responses, explore new hypothesis, or recognize previously
undiscovered phenomena.
• More effective in capturing intra-household features and non-income dimensions of poverty.
• Can identify particularly vulnerable subgroups.
• Allows respondents to articulate their own views.
Disadvantages
• Results not available for long period of time. • Findings difficult to generalize, and difficult to aggregate and compare systematically.
• Limited types of information can be gathered. • Fieldwork requires greater research skills than for quantitative enumeration.
• Can sometimes be expensive and
time-consuming.
Note: This table is intended to provide an indicative distinction between these methods and not a comprehensive description of individual techniques.
a. Social impact assessment adopts a more eclectic approach to data collection, choosing among open-ended, semi-structured, and close-ended instruments to fill information gaps
for mixed-method analysis.
Sources: Adapted from Carvalho and White 1997; Baker 2000; and World Bank 2002a.
15
A User’s Guide to Poverty and Social Impact Analysis
richer understanding of the impacts of policy on different subsets of the population, and to analyze
counter-intuitive results that might otherwise be dismissed as spurious. And a successful mixture can elucidate history, context, process, and identification of
transmission channels and differential impacts. While
mixed methods can involve higher costs, requiring
more complex skills and coordination with multidisciplinary teams, the benefits in some cases outweigh the
costs. As the work of Amartya Sen and others demonstrate, economics has contributed a great deal to, and
made liberal use of, qualitative analyses.
Likewise, the approach based on qualitative analysis
and open-ended data collection has particular
strengths. A variety of open-ended data collection
methods can be used to collect non-numeric information relevant to PSIA. Qualitative and contextual data
can be collected through participatory appraisals, asset
mapping, and structured interviewing of individuals,
communities, or focus groups. This information can
be used to undertake stakeholder analysis (discussed
above), participatory poverty assessment, beneficiary
assessment, institutional analysis, and risk analysis
(discussed below). Open-ended data collection methods such as those described in table 1 permit an interactive analytical process—one in which research
questions can be formulated, answered, and analyzed
iteratively in the field. The open-ended approach
allows subjects to articulate the research problem and
question. This interactive analytical process could
enable quicker turnaround and a shorter time lapse
between questionnaire design and analysis than closeended data collection methods and associated statistical analyses.7 Open-ended data collection methods
may also be undertaken using a random sample or a
purposive sample and may also be quantified to tabulate and analyze information.8
In undertaking PSIA there is much benefit to mixing and, where possible, matching elements of the
above approaches.9 This includes drawing on different
types of data collected by different techniques for multidisciplinary analysis. It is important to be aware that
economic analysis is not limited to quantitative analysis. Close-ended and/or open-ended data collection
techniques can be used to generate numeric and/or
non-numeric data, for analysis using quantitative
and/or qualitative techniques and approaches. Moreover, analytical methods can be mixed sequentially or
in parallel over time. Mixed methods can leverage the
benefits of both quantitative and qualitative analysis.
Qualitative analysis can inform the design of closeended questionnaires or the specification of an econometric model and generate hypotheses to be tested
further through quantitative research. Hypotheses
generated by qualitative analyses can be tested for generalizability using quantitative approaches. The results
of quantitative analysis can be further examined using
open-ended data collection methods to develop a
Taking stock of available data and analysis
The first element of the stocktaking is to ascertain the
existence of key data. This will allow identification of
data gaps that need to be filled or taken into account
when choosing an analytical approach. Household survey data are generally pivotal to undertaking quantitative poverty and distributional analysis.10 An
important consideration for poverty and social impact
analysis is whether, in addition to a welfare (e.g.
income/expenditure) aggregate, there is information in
the survey that provides the variable (or the computation of such a variable) related to the policy lever in
question—for example, household expenses on transport, or specifically public bus transport, if bus tariffs
are to be increased; or purchases of maize at subsidized
prices, if the subsidy is to be removed. Other important
sources of data include sector studies—which may
include administrative data, household survey data,
and qualitative information—and information on the
macroeconomic situation, including national accounts.
In analyzing policy reform, it is very useful, where possible, to test the robustness of conclusions by matching
data from different sources. This is often referred to as
“triangulation,” the practice of validating results
among three different sources. For example, in Armenia three different sources were used to compile and
compare information on consumption of, and expenditure on, utilities (using household survey data, utility
accounts data, and focus groups). Similarly, for particularly controversial issues, participants in discussion
groups may have an incentive to exaggerate or minimize certain impacts. Matching or triangulating results
is particularly important to validate such results.
16
Elements of Good Poverty and Social Impact Analysis
survey data. In the interest of building national capacity and enhancing ownership of the data and analysis,
where possible these data collection efforts should be
undertaken through national institutions, such as the
statistical agency, ministries, universities, or other
research organizations. A national household survey is
a large undertaking; it can take months to plan and
implement such a survey and analyze the resulting
data. Where possible, it is useful to identify planned
household surveys that are to be fielded imminently
and to add key questions relevant to the policy issue at
hand. These questions can leverage a wealth of analytical possibilities in the context of a full-fledged household survey.
Alternatively, there are now several “off-the-shelf ”
survey instruments that can be used to quickly collect,
enter, and analyze data (for example, the Core Welfare
Indicator Questionnaire, or CWIQ, survey). Social
impact assessment surveys, based on purposive sampling, can often be turned around in a shorter time
than a representative national household survey. Likewise, depending on the reform issue at hand, quantitative surveys can be employed using a purposive sample
(for example, among workers of a firm that is to be
downsized).11 When possible, use of mixed methods,
combining qualitative and quantitative analytical
approaches to triangulate results, helps to generate
richer and more robust findings. The use of data from
a non-representative sample to estimate parameters
may sometimes be required, and the “borrowing” of
parameters from other countries may also be needed.
Again, clearly stating assumptions (for example, that
these elasticities apply to the population at hand) will
be important in these instances. Care should be taken
when generalizing from such a purposive sample.12
Third, policymakers can rethink the policy decision
or the sequencing and pace of reform. One option is to
postpone the policy decision until adequate data can
be collected and appropriate analysis conducted. If
this course is taken, the costs of delaying reform (a
policy decision in itself) will need to be considered.
Other possibilities are to pilot or phase the reform, so
that progress can be monitored before a final decision
is made to implement a national program.
In the end, a tactical judgment will have to be
made as to how to proceed based on these consider-
Second, after identifying the availability of relevant
primary data, ascertaining the existence of analysis and
secondary data on the policy issue at hand is an obvious next step. In many instances, burning policy issues
have been the subject of analysis and debate in the past;
it is useful to draw on whatever analysis already exists,
and whatever public debate has already occurred. Project and program documentation, as well as data and
analyses from other development agencies, are invaluable. For sectoral reforms, information from existing
sector analysis, including administrative, household
survey, and qualitative data, can strengthen PSIA. Academic research and theses can also yield in-depth
insights not normally available in official reports.
Third, it is useful to ascertain and build the capacity of local agencies involved in data collection and
analysis (such as national statistical offices, ministries,
universities, research organizations, consulting firms,
NGOs, and so forth) to collect and analyze data.
Coping with PSIA data limitations
In many countries there are severe data limitations to
conducting poverty and social impact analysis. Some
or many of the desired data outlined above may simply
not be available. In this case, policymakers and analysts
will need to consider several options, outlined below.
First, they can adapt the analytical approach to data
currently available. If the urgency of policy action
severely limits the time available to gather further data,
expeditious analysis using the limited available data
may be required. Some tools and approaches to
poverty and social impact analysis are far less dataintensive than others. Adapting the analytical
approach to the available data, such as using time-use
data or focus group data to construct a simple household model, might be the best course of action. While
any analysis entails making assumptions, taking shortcuts generally means making more assumptions in
order to proceed. The analysis should be honest and
transparent in stating these assumptions. Qualitative
techniques, such as individual, community, or focus
group interviews, can be used to validate assumptions
and inform the design of quantitative surveys.
A second option is to collect more data. If critical
data gaps have been identified, it may be useful to
gather the data needed—whether administrative or
17
A User’s Guide to Poverty and Social Impact Analysis
Considerations in choosing approaches to impact
analysis
ations. This judgment will be influenced by the time
and resources at one’s disposal, which in turn will
depend critically on political and economic pressure
for action. In most cases, decisionmakers will not
want to embark on a major policy change without a
sound understanding of the poverty and social
implications of a policy action, particularly if such
action is aimed at reducing poverty. In some
instances, however, political or economic imperatives (as in a crisis situation) may lead policymakers
to take quick action. Where this happens, it will be
important to undertake PSIA as soon as feasible and
to consider measures to protect the poor from
adverse impacts and vulnerability to significant
risks (see section on compensatory measures,
below).
In general, four factors will condition the choice of
approach or tool to be used in analyzing the poverty
and distributional consequences of a given reform: the
importance of indirect impacts; data availability; time
availability; and capacity. For purposes of presenting a
simple typology, these four factors can effectively be
collapsed into two dimensions.
The first is the importance of indirect impacts. As
noted above, policy changes may have direct and/or
indirect impacts, depending on the reform in question
and the structure of the economy. A policy reform has
high indirect impacts if the net effect is transmitted
through several channels and markets, leads to behavioral changes at the household level, and/or has multiple round effects that may take time to work
themselves through the economy. An example could
be a massive devaluation that immediately results in
changes in relative prices, consumption, and power
structures, but over time might be expected to lead to
shifts in the structure of employment and the economy, changes in productivity, improved governance
(by removing rent seeking), and possibly growth.
Second is the availability of data, time, and local
capacity. As discussed above, data availability and
domestic capacity for data collection and analysis will
necessarily constrain the type of approach adopted.
The simple typology presented here collapses
data/time/capacity into a single dimension. Over time,
an objective of PSIA ought to be to improve the capacity of local practitioners and users. Wherever possible,
it is important that local partners—in the government
or outside organizations, as appropriate—become
involved both in selecting tools for analysis and in
applying them. This engagement can be the basis for
domestic capacity building, so that over time local
analysts rather than international specialists conduct a
larger share of the analysis.
Table 2 presents an indicative typology of how an
analyst may want to select an approach. It lays out a
choice of tools based on the importance of indirect
impacts for the reform in question, taking into consideration constraints of data, time, and capacity.13 This
table is only indicative, and the reality will vary
depending on the country circumstances and the
Addressing PSIA data limitations today so that they
do not limit future PSIA
When circumstances dictate that a policy decision
needs to be made without adequate data, it is important that steps be taken to improve the information
set over time. Since PSIA is necessarily a dynamic
process of formulating and adjusting policy based on
increased knowledge, it would also be important to
put into place a strategy to gather the necessary data
to enhance the basis for further and future (ex-ante
and ex-post) analysis of the poverty and social
impacts of policy. Such a strategy can be designed in a
manner that builds national capacity for data collection and analysis. Where possible, a strategy for data
collection should be linked to the timetable for policy
formulation, or for policy review and reformulation.
In other words, the reason for developing a strategy
for future data collection is not solely to permit expost monitoring and evaluation of a current policy
decision, but also to lay the groundwork for future exante analysis. Developing such a strategy is an integral
part of PSIA.
Element 6: Analyzing impacts
This section begins with general considerations in
choosing approaches to impact analysis and then provides an overview of several broad classes of methods
for estimating impacts.
18
Elements of Good Poverty and Social Impact Analysis
Table 2. Considerations in Choosing Impact Analysis Approaches
Data/Time/Local Capacity Availability
Low
High
High
• Beneficiary assessment
• Social impact assessment
• Participatory poverty assessment
• Benefit incidence analysis
• Social capital assessment tool
• Demand/supply analysis
• Household models
• Poverty mapping
• Social impact assessment
• Collect more data
• Use tools in adjacent cells in
conjunction with assumptions
• Multimarket analysis
• Reduced form
• Social accounting matrices
• Input/output models
• Computable general equilibrium
• Macro-model + micro-simulation
Indirect impacts
Low
Medium
Note: The tools presented along the dimension of “Data/Time/Capacity Availability” are additive across rows. That is to say, any tool that can be used in the context of lower
data/time/capacity can also be used with higher data/time/capacity, and certain tools, such as social impact assessment, can be applied to examine higher indirect impacts.
Once the relevance of indirect impacts has been
determined, the next consideration will be the availability of data, time, and capacity. Where these are in
short supply, the analysis might need to use simpler
tools and methods in the short term. In such cases, an
action plan to strengthen data and capacity should be
put in place for more robust analysis in the future. This
way countries in the “low” data and capacity situation
could aim to improve their information base so they
have the option of adopting methods in the “medium”
and “high” columns, as appropriate. (See Annex for
data, time, and skill requirements for each tool.)
PSIA can utilize various methods and tools, many
of which require the combined skills of various disciplines (for example, macroeconomics, microeconomics, social and political analysis). Where feasible, it is
advisable to integrate economic and social analyses in
order to deepen the analysis. For instance, social
impact assessments can be used to help define the
parameters and explanatory variables used in econometric modeling, and conversely, an understanding of
economic dynamics and constraints can strengthen
the social analysis of a given policy.
The rest of this section briefly lays out the different
social and economic tools for PSIA, and the reforms to
which they are best applied. It first presents tools for
social analysis, which can be used in conjunction with
reform in question. Choices will therefore have to be
made on a case-by-case basis.
In contemplating the choice of tools, a helpful first
step is to consider whether the reform in question is
likely to have low or high indirect impacts. The answer
will depend partly on the scale of the reform and its
importance to the economy, as well as the time horizon. With regard to the latter, elasticities are typically
lower in the short run than in the long run. For
instance, a tax reform may have low indirect impacts
in the first year of implementation, but much larger
ones in subsequent years as agents adjust to the new
tax rates. As another example, the indirect impact of
utility reforms could be very low, in the case of
changes in tariffs paid only by a handful of rich consumers—or they could be very significant, as with the
wholesale restructuring of the electricity sector in an
industrial country. Moreover, the impact of individual
reforms may be low, but if they are taken as a package
the combined impact could be high.
While country circumstances and reform specificities will ultimately determine the strength of indirect
impacts, it is possible to broadly classify specific
reforms as having lower or higher indirect impacts,
based on the scale on which they are undertaken in
most low-income countries. Box 5 provides an indicative breakdown.
19
A User’s Guide to Poverty and Social Impact Analysis
Box 5. Illustrative Categorization of Selected Reforms according to
Scale of Indirect Impacts
This categorization is indicative only: actual indirect impacts of a
given reform will ultimately be driven by country circumstances,
including the scale and complexity of the policy adjustment.
•
•
Reforms with typically higher indirect impacts
• Macroeconomic and fiscal reform: monetary policy
reforms, affecting inflation and interest rates; broad external policy, affecting balance of payments and reserves; and
broad fiscal policy, affecting fiscal deficits.
• Trade and exchange rate reform: reform of tariff and nontariff barriers; exchange rate adjustments.
• Agricultural reform: elimination of administered prices;
changes in domestic subsidies and taxes; abolition of marketing boards.
• Financial sector reform: liberalization of interest rates; allocation of credit; lowering barriers to entry; regulatory reform.
•
•
•
•
•
revenues; improvements in tax administration; cost recovery.
Land reform: distribution to landless; changes in legal
rights to own, exchange, and inherit land.
Utility reform: restructuring of state-owned utilities;
increased private participation; full divestiture.
Financial sector reform: privatization/closure of state
banks; promotion of financial institutions serving the
poor.
Privatization: lease of assets; private management contracts; full divestiture.
Civil service retrenchment: layoffs, reductions in the wage
bill.
Decentralization of public services.
Social safety nets: changes in targeted cash/in-kind transfers; benefits to needy groups (such as AIDS orphans);
social insurance benefits.
Pensions: scaling back pay-as-you-go public schemes;
increased private provision; introduction of social pensions
(cash assistance for poorest pensioners).
Reforms with typically lower indirect impacts
• Public finance reform: changes in allocation and level of
public expenditures; changes in level and composition of
•
either direct or behavioral analysis methods and/or to
inform the approaches for indirect impacts. It then
reviews the two broad economic approaches to analyzing direct impacts: direct impact analysis and behavioral
analysis. Finally, the section reviews complementary
economic approaches to analyzing indirect impacts:
The first covers macroeconomic frameworks that aim at
modeling the different impacts of policy interventions
on a variety of sectors or markets, but that leave open
the distributional implications of policy changes. These
frameworks are either partial equilibrium analysis or
general equilibrium techniques. Then, the second group
comprises tools that use as inputs the results of any of
the macroeconomic frameworks, and assess the distributional implications of policy changes: Tools linking
microeconomic distribution or behavior to macroeconomic frameworks or models. Under each class of methods, the discussion presents an overview of specific
tools (referred to in bold text) that are discussed in
greater detail in the annex (including their data requirements and particular advantages and shortcomings).
impacts with behavioral analysis.14 These tools analyze
how people are likely to be affected by reform, how
this impact will differ among groups (based on gender
or ethnicity, for example), what coping mechanisms
people have to deal with changes effected by reform,
and who is most likely to be vulnerable to a particular
reform. In addition to the analysis of direct impacts,
social analysis typically also includes an evaluation of
how different people are likely to respond to a reform
(behavioral response), and some of the institutional
constraints the reform may face during implementation. In addition to demand and supply analyses,
which are multidisciplinary tools typically carried out
using a combination of qualitative and quantitative
techniques (presented below under “behavioral analysis”), three broad classes of methods fall within the
repertoire of social analysis for policy reform: social
impact assessment, participatory poverty assessments,
and the social capital assessment tool. The choice
among methods depends on the particular policy and
the time available for research.
Social impact assessment (SIA) is used to assess how
the costs and benefits of reforms are distributed
among different stakeholders and over time. It is particularly useful in understanding how the assets (phys-
Social analysis
The first approach consists of several techniques of
social analysis that combine understanding of direct
20
Elements of Good Poverty and Social Impact Analysis
2001). They are more relevant to broad-based
fiscal/expenditure and sectoral reforms with potential
impacts on livelihoods and vulnerability (Dulamdary
and others 2001). BAs tend to use similar qualitative
data-gathering techniques, but they focus specifically
on consultation with those groups directly affected by a
specific intervention, project, or policy, and therefore
have not typically looked for national representativity.
They do not focus specifically on the poor.
The social capital assessment tool (SOCAT) measures
social capital (institutions and networks, and their
underlying norms and values) at the level of households, communities, and key organizations. It allows
analysts to identify how these social assets affect productive behavior (for example, income generation and
risk management), and how this in turn responds to
policy reform. For instance, well-functioning networks
with high levels of trust, such as among parent-teacher
associations or farmer associations, may facilitate policy
changes that call for collective action or cooperation.
Alternatively, SOCAT data make it possible to assess
whether certain policies strengthen or undermine social
assets. The tool can be tailored to specific policies or
used to give depth to other methods of data collection
and analysis. A tailored version of the SOCAT survey
was administered in Bosnia and Herzegovina, where
measurement of the level of social capital led to recommendations for reform of the social welfare system, and
improvements in service provision and the integration
of returning refugees (World Bank 2002b).
ical, financial), capabilities (human, organizational),
economic and social relations (e.g. gender, exclusion)
of stakeholders, and the institutional mechanisms
through which policy actions are transmitted, affect
policy outcomes. Stakeholder analysis is a prerequisite
for SIA. When reasonable national survey data exist,
SIA uses a range of qualitative data collection tools
(focus groups, semi-structured key informant interviews, ethnographic field research, stakeholder workshops) to determine impacts, stakeholder preferences
and priorities, and constraints on implementation. In
the absence of adequate quantitative data, SIA supplements qualitative, sociological impact analysis with
purposive surveys that capture direct impacts and
behavioral responses to reform, or specific dimensions
(such as time-use patterns) that affect reform outcomes (the “low-low” cell in table 2). SIA can be used
to examine the impacts of structural reforms such as
privatization of state-owned enterprises, agricultural
reform, reform of basic services, utility reform, civil
service reform, and fiscal policy. It is particularly relevant for understanding the quality of impact on different groups, and examining how the poor cope with
reforms and access market opportunities. Given the
overlap of research methods, SIA is more cost-effective
when undertaken simultaneously with institutional
analysis and social risk assessment.
Participatory poverty assessments (PPA) and beneficiary
assessments (BA) both rely on direct consultation of specific groups and field observation, using primarily qualitative techniques (focus groups, key informant
interviews, and a range of other tools classified under
the broad label of participatory rural appraisal). Like
poverty maps, PPAs have often been used before the
analysis of a specific policy reform to identify those
policies and issues of most relevance to the poor, and to
understand the non-income dimensions of poverty and
the processes through which reform actions filter down
to the poor. PPAs tend to focus on information and
analysis at the national level by selecting a sample of
regions for intensive research on poor people’s views, in
order to understand poverty impacts through a series of
rapid assessment tools and structured task-based analytical exercises. They can be adapted for use in monitoring or seeking feedback on a particular policy and in
designing pro-poor public policies (Norton and others
Direct impact analysis
Direct impact analysis is a simple assessment of who is
directly affected by a policy change, and how much
they are affected. It assumes no behavioral response
from affected households or groups; that is, if prices
change, quantities do not adjust. Effectively all elasticities are assumed to be zero, including own-price elasticities. This assumption is appropriate for assessing
short-term impacts, before economic agents have time
to make adjustments. It otherwise represents a limitation of the approach. In particular it will tend to overstate the impact on household welfare. The approach
can be used to analyze any type of policy change—for
example, a change in prices (such as a commodity
price, tariff, wage, or exchange rate) or a change in
21
A User’s Guide to Poverty and Social Impact Analysis
Applications also include planning of public investments in education, health, and transport, and targeting of direct social assistance and food aid to
vulnerable populations. The method is most useful
when constructed at a fine level of disaggregation, but
this requires very large data sets.
Tools to assess public service delivery allow analysts to
measure the efficiency of public spending and the
delivery performance through assessing leakages and
their sources, captures of financial flows, and incentives and accountability mechanisms at all stages of the
expenditure chain. This complements incidence analysis, which relies on analyzing the cost of the services
provided, irrespective of the service that actually
reaches the beneficiaries. Applications of these tools
include the analysis of the efficiency and quality of
health and education service delivery in Tanzania and
Uganda (Government of Tanzania, 1999 and 2001 and
Reinikka, 2001). These tools, including Public Expenditure Tracking Surveys (PETS) and Quantitative Service
Delivery Surveys (QSDS), are described in more detail
in the section on monitoring and evaluation and in
box 13, and are presented in the Annex, under the
“monitoring and evaluation” section.
public finance policy (such as an expenditure program
subsidy, tax, civil service or state-owned enterprise
retrenchment). But it is best suited to reforms whose
impacts are mainly short-term. Examples include the
removal of a subsidy, a small-scale privatization, or a
single price change in a relatively isolated market.
Below are three examples of tools that fall within
this approach: incidence analysis, poverty mapping,
and tools to assess public service delivery. These range
in terms of data/time/capacity requirements from low
to high, as shown in table 2, with poverty mapping by
far the most demanding.
Incidence analysis estimates the distributional incidence of a component of income or expenditure at the
household level. The analysis is an appropriate starting
point where quantitative data are available (the “lowmedium” cell in table 2). A useful first step is to examine key descriptive statistics for the country to see
which households are “exposed” to the policy change.
The most common application is in relation to tax and
expenditure reform; the technique has been used, for
instance, to estimate the incidence of education
expenditure in Malawi. It can also be used for reforms
that affect prices and consequently household
incomes, such as utility or agricultural reform. Applications of this type include access to utility services in
Guatemala (Foster and Araujo, 2001). There are two
main types of incidence analysis relevant to the direct
impact analysis: simple incidence analysis and marginal
incidence analysis. The first measures the incidence of
average expenditure or tax, that is, it considers all
expenditure or taxes. The second focuses on the distributional incidence of the last or next unit of expenditure or tax (see box 6).15
Poverty maps are geographical profiles that show the
spatial distribution of poverty within a country, and
suggest where policies might have the greatest impact
on poverty reduction. Poverty maps can be used to
illustrate outputs of most analytical tools. For
instance, a poverty map can be combined with maps
that show the placement of primary health care facilities to understand the access to health services by the
poor. The technique is particularly suited to reforms
with regionally differentiated impacts such as decentralization and agricultural reform, as in the case of
rice price changes in Madagascar (Mistiaen, 2002).
Behavioral analysis
Behavioral analysis includes economic tools that go
beyond direct impact analysis to recognize some
behavioral responses among households and economic agents. Behavioral analysis includes methods
that permit non-zero own-price and cross-price elasticities. In other words, with a price or other policy
change, households may switch to consuming or producing other goods and services and move along their
respective demand or supply curves. The approach is,
however, limited to a purely “micro” focus. Namely,
supply is not equated to demand in a market, markets
do not clear, and prices are therefore not endogenous.
Rather, households simply react to an exogenous policy shock based on behavioral specifications and
assumptions. If data, time, and capacity permit,
behavioral analysis should always supplement simpler
incidence analysis to more fully illuminate household
responses to policy change. Some of the tools of
behavioral analysis are behavioral incidence analysis,
demand/supply analysis, and household models.
22
Elements of Good Poverty and Social Impact Analysis
Box 6. Impact of Public Expenditures in Indonesia: Average versus
Marginal Benefit Incidence
A similar exercise was carried out for junior and senior secondary education and indicated that benefits of public spending for higher education levels become increasingly regressive.
In health, per capita transfers on primary health care were
found to be rather evenly distributed across quintiles, while
government spending on hospitals was highly regressive.
Average and marginal benefit incidence has been examined by
Lanjouw and others (2001) to assess how education and health
expenditures affect different income groups in Indonesia. Static benefit incidence analysis entailed dividing groups into
expenditure quintiles and computing rates of utilization of the
facilities for each group. For primary education, total government outlays in 1998 amounted to nearly 8,000 billion rupiah
(covering both routine and development expenditures). In that
year there were just over 25 million students enrolled in public
primary schools. Assuming uniform transfers, the government
thus transferred some 307,000 rupiah per public primary student per year.
The authors also considered the marginal benefit incidence of
public expenditures. In other words, they asked how a change in
government spending would be felt across expenditure groups.
First the incidence of changes in education and health provisioning across two periods of approximately a decade each was
analyzed. Second, the quintile-specific “marginal odds ratio” of
participation—defined as the incremental increase in the quintile-specific participation rate associated with an aggregate
change in the program participation rate—was estimated on
the basis of survey data. This was compared with the “average
odds ratio”—the quintile-specific participation rate in a given
year relative to the participation rate for the population. On the
basis of the historical analysis as well as the estimation results,
the evidence suggests that changes in public spending on primary education would be even more strongly felt in the bottom
two quintiles than what static analysis would suggest.
The table below gives the incidence of government primary
education spending for each expenditure quintile. As can be
seen from the table, government expenditure has a pro-poor
distribution, with an average per capita transfer of around
47,900 rupiah for the lowest quintile and 25,300 for the highest quintile. With practically universal enrollment, the propoor bias is largely driven by the fact that poorer households
tend to have more young children than other households (6.2
million primary school students in the lowest quintile, versus
3.3 in the highest quintile).
Population age 7–12
(millions)
Public school students
(millions)
Average per capita
transfer (rupiah)
Percent of total
1
2
Expenditure Quintile
3
4
5
Total
6.8
6.2
5.4
4.8
3.8
27.0
6.2
5.9
5.2
4.5
3.3
25.2
47,898
24.0
45,324
23.5
40,004
20.7
34,375
17.8
25,270
13.1
38,574
100.0
marginal incidence analysis, the ex-ante behavioral marginal
evaluation of policy reforms, and the ex-post evaluation of
assigned programs.
Demand and supply analyses estimate the responses of
consumers and producers, respectively, to price
changes. Demand analysis can assess the willingness of
consumers at different income levels to pay for public
services like water and electricity. It has been used to
assess the impact of higher electricity tariff rates in
Armenia (box 7), and is being applied to the same
issue in the Kyrgyz Republic. It has also been used to
evaluate preferences and likely responses of water consumers to tariffs and institutional reform such as privatization in several African countries (Mozambique,
Lesotho, Angola, and Zambia). Supply analysis is most
Behavioral incidence analysis combines incidence
analysis, presented above, with econometric estimates
of household behavior. It can be used to explain distributional changes arising from a policy change, and
thereby addresses one of the shortcomings of incidence analysis. Applications have included analysis of
the role of government policy (in relation to the private sector) in expanding access to education in
Malaysia (Hammer, Nabi, and Cercone 1995); examination of the disincentive effects of food stamps on
labor supply in Sri Lanka (Sahn and Alderman 1995);
and study of the crowding out of private transfers by
public funds in the Philippines (Cox and Jimenez
1995) and South Africa (Jensen 1998). The Annex
present details on techniques for the ex-post behavioral
23
A User’s Guide to Poverty and Social Impact Analysis
their equilibrium level.16 Thus prices are now endogenous. Partial equilibrium analysis is distinguished
from general equilibrium analysis (discussed below) in
that it does not include all production and consumption accounts in an economy, and does not attempt to
capture all markets and prices in an economy. Partial
equilibrium approaches (which include elasticities on
both the demand and supply sides) will allow for indirect impacts that occur when changes in one market
affect other markets, but they will only capture these
changes to the extent that they include the relevant
markets.17 This is their biggest drawback relative to
general equilibrium approaches. For this reason, partial equilibrium analysis is better suited to analyzing
sectoral reforms (such as agricultural marketing and
pricing and utility pricing reforms) that are less likely
to have large impacts on macro aggregates. Partial
equilibrium techniques fall within the “high-medium”
category of table 2 in that they at least require household survey data. Tools for partial equilibrium analysis
are multimarket models and reduced form techniques.
suited to analyzing agricultural reforms that affect the
poor in their role as producers, and has been used to
examine the impact of agricultural liberalization on
poor farmers in Mexico (box 8). Supply and demand
analyses are typically carried out using a combination
of qualitative and quantitative techniques.
Household models are somewhat more complex, in
that they analyze impacts by taking account of households as both consumers and producers. The models
integrate producer, consumer and worker decisions
into a household problem, to reflect the fact that many
households, especially in rural areas, are simultaneously units of production and consumption. They are
particularly suited to addressing agricultural reforms,
but have been used in relation to large sets of reforms,
including taxation.
Partial equilibrium analysis
Partial equilibrium analysis goes a step beyond behavioral impact analysis in that it equates supply and
demand in one or more markets so that prices clear at
Box 7. Impact of Utility Pricing on the Poor in Armenia: Demand Analysis
policy tradeoff involved in raising tariffs, which can help cover
costs but also threatens to reduce household consumption.
A recent study (Lampietti and others 2001) uses multivariate
welfare analysis to assess the poverty impact of raising tariffs
in the electricity and water sectors in Armenia. It looks ex post
at the impact of higher electricity prices (and an accompanying expansion in social safety net provision) and ex ante at
increased water tariffs. The study estimates a demand function
to examine consumers’ responses to changes in prices, including through substitution of other forms of fuel for electricity.
Possible supply side adjustments (to the cost and structure of
production) are not taken into account.
In both cases results from survey data are corroborated against
the predictions from multivariate models of household expenditure per head. The models include as explanatory variables
demography, asset holding, and regional location; each is estimated separately for rural and urban households.
The electricity study finds that households cut their consumption and switched to wood and natural gas alternatives as a
result of the rate increase. This effect was particularly marked
for poor households. As a result, the reform has produced only
a modest improvement in revenue. One policy implication is
that future tariff rises are more closely aligned with likely consumer responses. Another is the need for action to mitigate
poverty and environmental impacts.
The analysis draws on two specially commissioned surveys,
undertaken over the course of the electricity reform: a quantitative household survey of water and electricity consumption
patterns (as well as of standard information on income and
demographics), and a qualitative consumer satisfaction survey
based on focus group research concerned with attitudes
toward provision. For electricity, the data are matched with
administrative statistics on payment and consumption.
The results of the water analysis suggest that consumers are
reluctant to pay significantly more for a service they deem
unreliable. The authors suggest that reform should, therefore,
proceed in two stages—first enforcing payment from households with reliable service, and then raising tariffs incrementally to balance cost recovery with the need to maintain access
of poor users.
The electricity study examines changes in consumption and
payment behavior (pattern of arrears, and so forth) of poor and
non-poor households following reform. The water analysis
considers (a) how much extra non-poor and poor households
would be willing to pay for an improved service, and (b) the
24
Elements of Good Poverty and Social Impact Analysis
Box 8. Impact of Liberalization in Mexico: Supply-Side Analysis
had less access to and more problems with credit, and were less
likely to use purchased inputs, such as seeds, fertilizer, pesticides, or to use a tractor for soil preparation. Their land was of
lower quality on average, and their educational level lower, than
those with greater productive assets. This analysis predicted
that poor farmers would benefit less from liberalization.
Simple supply-side estimation can be used to examine the differential impacts of policy change on welfare. López, Nash, and
Stanton (1995) use a household survey from Mexico to estimate
the relationship between household assets and agricultural supply response. At the time, the Mexican economy was becoming
increasingly open – markets for inputs, outputs, and credit were
being liberalized. The study had two related goals. The first was
to monitor the condition of Mexican farmers, especially the
poor, and see how they had been affected by changes in policy
and environment. The second goal is to understand the constraints facing the ability of poor households to adjust to the
new regime and take advantage of new opportunities.
However, the results of the smaller “panel” study suggest that
conditions had improved both on average, and for the poorer
farm households in the sample. Cropping patterns are more
diverse, landholdings have increased, as has the use of purchased inputs, and asset ownership has also improved modestly. They also find that among the poor, educational
attainment and off-farm income have declined. Although
López et al. (1995) do not speculate, this may be due to the
greater returns to on-farm labor brought about by liberalization, which reduce (relatively) the returns to off-farm income
and the educational investments necessary to enter the offfarm labor market.
López and others (1995) construct a model showing that own
wealth affects both the level of output and the ability to respond
to price changes. They test this model against a large “baseline”
dataset from 1991 and a smaller selected survey from 1993.
Using the baseline data, they find that farmers with fewer productive capital assets (the “poor”) grew fewer crops on average,
Source: López, Nash, and Stanton (1995).
Multimarket models permit the combined estimation
of systems of supply and demand relationships, so that
the analyst can see how policies in one sector impact
on other related sectors. Multimarket models represent a simpler alternative to computable general equilibrium models, and have been used, for example, to
examine the welfare impact of technical change in
agriculture, increased exports, and input subsidies in
India (Binswanger and Quizon 1984, 1986) and agricultural subsidies and tariffs in Turkey (see box 9).
Reduced-form estimation can be used to simulate the
impact of different policy variables on poverty and
social outcomes. The approach is less data-intensive
than multimarket modeling. For instance, reduced
form techniques were used to study rural poverty in
Zambia, taking advantage of household budget data,
time-use information, and other sociological and
anthropological data.
the economy, in varying degrees of aggregation. In
theory, a well-specified general equilibrium model can
capture indirect impacts of policy generated from all
other markets. However, in practice, as with any economic estimation, it captures indirect impacts only
from those markets that are included in the model,
and results depend on the model specification and
parameters.18 While general equilibrium analysis can
be used to analyze most types of policy reform, it is
most relevant to reforms with multiple and significant
indirect impacts on the economy through a number of
transmission channels. An exchange rate devaluation
or alternative aggregate fiscal policies would be best
assessed with a general equilibrium approach, data
and capacity permitting. General equilibrium analysis,
in capturing accounts from the entire economy,
requires not only household survey data but also comprehensive and consistent national aggregate data. The
computational and capacity requirements are also
generally high. Other drawbacks are that the technique
can be difficult to explain to policymakers, and results
are sensitive to the assumptions on which a particular
model is based. The approach hence is presented in the
“high-high” cell in table 2. Specific tools for general
equilibrium analysis are social accounting matrices
General equilibrium analysis
General equilibrium analysis goes beyond partial equilibrium analysis in that it models all economic
accounts in the economy and thus aims to present a
comprehensive picture. What the methods in this category have in common is a complete specification of
25
A User’s Guide to Poverty and Social Impact Analysis
Box 9. Impact of Agricultural Subsidies and Tariffs in Turkey: Multimarket Modeling
Hammer and Tan (1989) constructed a multimarket model of
the agricultural sector in Turkey. Their model contains eight
separate agricultural markets, all of which are potential substitutes for each other. Some of these are traded internationally.
Incomes in the rural areas are derived from agricultural profits. The model also includes an explicit government account,
which taxes, provides subsidies, and intervenes directly in the
markets for selected outputs. Elasticities for supply and
demand were taken from published sources, and modified to
satisfy theoretical restrictions and to conform to base data.
Sensitivity analysis confirmed that the model was robust to
large changes in these and other assumptions.
The model simulates the impact of changes in government policy concerning direct intervention (subsidies and support
prices) and tariffs. The results indicate that reducing export
taxes leads to a broad-based increase in supply and exports, and
that the incidence of subsidies to fertilizer and feed grains is sufficiently skewed that they could be cut without damage to farm
incomes or export earnings. Also, import duties on milk products are regressive. Imposing border prices (removing import
tariffs and restrictions) leads to improved government finance
and foreign exchange earnings. It also improves the incomes of
middle and wealthy farm households, but at the risk of harming
consumers—especially the poor—through higher prices.
and input-output models and computable general
equilibrium models.
Social accounting matrices (SAM) and input-output
(IO) models can be used for simple policy simulations
(by selecting some accounts as exogenous, and leaving
the others endogenous). For instance, in a SAM containing agricultural production and transportation
accounts, the impact of an exogenous change to agriculture can be simulated (leaving transport fixed) or
the other way round.19 SAMs have some serious limitations, including the facts that prices do not adjust to
reflect changes in real activity, and results are highly
sensitive to which accounts are assumed to be endogenous and which exogenous.
Computable general equilibrium (CGE) models are completely specified models of an economy (or a region).
They vary in their complexity from the basic 1-2-3
model (one country, two activities, three goods) to
versions with several activities and actors and hundreds of parameters. CGEs can be used in a number of
policy contexts, including public finance reform and
macroeconomic stabilization.20 Box 10 illustrates the
use of a CGE model to calculate the impact of fiscal
incidence in the Philippines. As well as being dataintensive, CGEs—even simple ones—can be difficult
to build and understand.
and poverty outcomes are arrived at iteratively and
outside the macro-modeling exercise. In its simplest
form, the macroeconomic framework/model (such as
any of those reviewed above) is solved to derive the
main equilibrium parameters (such as prices, wages,
fiscal deficit, and so forth); these parameters are then
fed into a micro-model. There are several micro
approaches that can be used to derive poverty and distributional outcomes based on the parameters derived
from the macroeconomic framework/model.21 The
approaches presented below can be applied to a wide
variety of reforms. However, they are data- and skillintensive, and are located in the “high-high” cell in
table 2.22 The following specific techniques are
described in more detail in the annex.
Linking macro-framework to a reduced form estimation is
a minimalist approach that simulates poverty impacts
on the basis of various macroeconomic variables.23
Tools have also been developed to examine how
changes in certain macro-variables—most particularly
growth rates—affect poverty, based on a countryspecific distribution. SimSIP and PovStat are tools of
this type.24
Linking macro-framework to behavioral analysis estimated for representative households has been done in the
1-2-3 PRSP model, which links the 1-2-3 model to a
behavioral analysis of representative households
(Devarajan and others 2001), and PAMS, which joins a
labor-poverty module to a macro-consistency model
(such as the Bank’s RMSM-X).25 The technique can be
used to simulate a wide range of policies, from labor
and wage policies to taxation, prices, and the alloca-
Tools linking microeconomic distribution or behavior to
macroeconomic frameworks or models
The last class of tools and methods links microeconomic behavior and/or distribution with a consistent
macroeconomic framework or model. Distributional
26
Elements of Good Poverty and Social Impact Analysis
Box 10. Net Fiscal Incidence in the Philippines
of tax with the CGE model. The incidence reflects both actual
tax collections and the increased costs associated with each tax.
The effective rates for indirect and direct taxes were aggregated
to obtain overall tax burden.
Ideally, one should be able to analyze the incidence of tax and
expenditure policies simultaneously, that is, conduct a net fiscal incidence analysis. In practice, this type of analysis is difficult to undertake because the data requirements are extensive.
Devarajan and Hossain (1998) completed one of the few examples of this type of analysis in the Philippines. The net incidence of fiscal policy (indirect taxes, direct taxes, and
expenditures) was estimated using a variety of data sources
and tools.
For expenditures, the authors focused on health, education,
and infrastructure spending. Nationwide incidence patterns
were derived from regional patterns of expenditures along
with information on income distribution. To derive benefit
incidence, the authors inferred the implicit subsidy on health,
education, and infrastructure for each income decile. Overall
incidence of public expenditures in health, education, and
infrastructure was calculated as the weighted average of the
regional incidence, with the weights being the regional allocations of these expenditures. Total incidence of public expenditures was calculated as benefits as a share of gross income.
For both direct and indirect taxes, the authors calculate the
effective tax rate for each income decile defined as the change
in purchasing power of each income class. For direct taxes, they
calculated the effective tax rate using actual tax collection rates
broken down by gross income. The family income and expenditure survey was used to map income classes into deciles. For
indirect taxes, a multisector CGE model was used to calculate
the incidence of taxes. The effective tax rate for each type of tax
(such as VAT, import tariffs, excise taxes) was calculated individually. This was done by simulating the removal of each type
The results indicate that tax incidence is fairly neutral. Expenditure incidence is strongly progressive, as is the combined
incidence.
tion and levels of government spending. Applications
include the linking of a simple CGE model with a
demand system for food to examine the impact of
macroeconomic policy changes on food consumption
and nutritional status in the Philippines (Orbeta and
Alba 1998).26
A third technique is linking macro-framework to microsimulation. A more disaggregated variant of the representative household method above is to simulate
behavior at the level of the individual household.
Robillard, Bourguignon, and Robinson (2001) use this
approach to analyze the poverty impact of the Indonesian financial crisis (see box 11). Their household
model is linked to a CGE through wages, and the sectoral allocation of employment and prices. It is constrained to be consistent with the output of the CGE.
ter understanding of adverse impacts can also inform
the design of appropriate compensation mechanisms,
if needed. This component of the PSIA is informed by
the analysis and tools laid out in the previous section.
This analytical work can provide potential options to
limit the negative impacts on the welfare of the poor or
other groups. In addition, finding the appropriate solution, or set of solutions, also often necessitates substantial discussion and debate by key stakeholders, in
particular consultation with those affected to test
whether the proposed compensation measures can feasibly be implemented. In short, if the ex-ante poverty
and social impact analysis shows that a proposed
reform will have short-term adverse impacts on the living standards of the poor or other groups, it is critical
that the analyst address the following considerations.
Element 7: Contemplating enhancement
and compensation measures
Consider alternative design
The design of reform may be improved by including
enhancement or mitigation measures, or by different
sequencing of public actions. First, one may opt to
proceed with the implementation of a reform as
planned, but with a subsidization arrangement to protect the poor or others adversely affected by the policy.
For example, a water tariff increase associated with
utility reform may be designed to protect those who
Poverty and social impact analysis is undertaken to
maximize welfare gains, in particular for the poor, by
influencing the design of a policy reform. To the extent
that there are losers from the reform, PSIA can inform
policy design leading to choices that minimize the
number of losers or the extent of adverse impacts. Bet27
A User’s Guide to Poverty and Social Impact Analysis
Box 11. Impact of the Indonesian Financial Crisis on the Poor: Partial Equilibrium
Modeling and CGE Modeling with Micro-simulation
items. Second, the results are exclusively nominal, in that the
welfare changes are due entirely to changes in the price of consumption, and do not account for any concomitant change in
income. Third, this analysis cannot control for other exogenous events, such as the El Niño drought and resulting widespread forest fires.
General equilibrium models permit the analyst to examine
explicitly the indirect and second-round consequences of policy changes. These indirect consequences are often larger than
the direct, immediate impact, and may have different distributional implications. General equilibrium models and partial
equilibrium models may thus lead to significantly different
conclusions.
Robillard, Bourguignon, and Robinson use a CGE model, connected to a micro-simulation model. The results are obtained
in two steps. First, the CGE is run to derive a set of parameters
for prices, wages, and labor demand. These results are fed into
a micro-simulation model to estimate the effects on each of
10,000 households in the 1996 SUSENAS survey. In the microsimulation model, workers are divided into groups according
to sex, residence, and skill. Individuals earn factor income
from wage labor and enterprise profits, and households accrue
profits and income to factors in proportion to their endowments. Labor supply is endogenous. The micro-simulation
model is constrained to conform to the aggregate levels provided by the CGE model.
A comparison of conclusions reached by two sets of
researchers, examining the same event using different methods,
reveals the differences between the models. Levinsohn, Berry,
and Friedman (1999) and Robillard, Bourguignon, and Robinson (2001) both look at the impact of the Indonesian financial
crisis on the poor—the former using partial equilibrium methods, the latter using a CGE model with micro-simulation.
The Levinsohn study used consumption data for nearly 60,000
households from the 1993 SUSENAS survey, together with
detailed information on price changes over the 1997–98 crisis
period, to compute household-specific cost-of-living changes.
It finds that the poorest urban households were hit hardest by
the shock, experiencing a 10–30 percent increase in the cost of
living (depending on the method used to calculate the change).
Rural households and wealthy urban households actually saw
the cost of living fall.
These results suggest that the poor are just as integrated into
the economy as other classes, but have fewer opportunities to
smooth consumption during a crisis. However, the methods
used have at least three serious drawbacks. First, the consumption parameters are fixed, that is, no substitution is permitted
between more expensive and less expensive consumption
The Robillard team finds that poverty did increase during the
crisis, although not as severely as the previous results suggest.
Also, the increase in poverty was due in equal parts to the crisis and to the drought. Comparing their micro-simulation
results to those produced by the CGE alone, the authors find
that the representative household model is likely to underestimate the impact of shocks on poverty. On the other hand,
ignoring both substitution and income effects, as Levinsohn,
Berry, and Friedman do, is likely to lead to overestimating the
increase in poverty, since it does not permit the household to
reallocate resources in response to the shock.
consume relatively small quantities of water by incorporating a subsidy mechanism.27 Often contextual
information and consultations are required to select
the most appropriate type of mechanism to best fit
specific country circumstance and implementation
capacity. Alternatively, analysis of an electrical utility
reform may determine access to be the main constraint for the poor, resulting in the design of subsidized grid connection fees for targeted poor
communities.28 In fiscal reform, key staple goods that
make up the bulk of consumption for the poor may be
exempted from taxation.29
Second, the policy set may need to be expanded
beyond the core policy measures (driven by the problem diagnosis) to include complementary measures.
For example, if “behind the border” bottlenecks (such
as barriers to entry in the domestic transport sector)
reduce the benefits of trade liberalization accruing to
intended beneficiaries, taking measures to address
those constraints will be critical to achieving expected
welfare gains. Similarly, it will be essential to understand and address the factors that constrain the poor
or other target groups from benefiting from market
reforms—for example, lack of assets (land, credit,
electricity grid connection) or of capabilities (price
information, market access). Micro-econometric
analysis as well as qualitative analysis can assist in
identifying the type of complementary measures that
might be necessary.
Third, it is important to carefully consider sequencing. For example, shutting down a commodity board
can eliminate monopsony and subsidized inputs at the
28
Elements of Good Poverty and Social Impact Analysis
calculate the cost of compensation, and consider it relative to the expected benefits of reform. In terms of
costs, the compensation scheme itself (for example, a
large retrenchment or social program) will have fiscal
costs that, depending on magnitude, can have indirect
impacts on fiscal stability, prices, and the economy.
Moreover, there is an opportunity cost, as any compensation scheme will use resources that would otherwise have been spent elsewhere.30
same time. If critical inputs are likely to be unavailable
or prohibitively expensive for vulnerable farmers in
certain locations, PSIA might suggest that the government first take action to drop barriers to entry or
encourage private merchants to pursue untapped
markets before it dismantles the commodity board.
Also, sustainability of the reform process can be
enhanced with quick wins among key stakeholders to
build support for reform. For example, new resources
for mining safety in Russia were used to persuade the
unions of the need for reform.
Consider delay or suspension
If the findings of PSIA suggest that the short-to-longterm benefit of the best-designed policy intervention
does not exceed the short-term (or long-term) costs of
mitigating or compensating the poor, or that other
important groups might suffer irreversible losses, then
consideration could be given to delaying the reform
(that is, resequencing) or abandoning or suspending
implementation of the policy.
Consider direct compensatory mechanisms
When adverse impacts of reform are unavoidable, considerations driving the decision to compensate losers
may be based on: (a) poverty grounds (especially if
some of the poor lose in the short run and the objective of the policy is poverty reduction); (b) equity
grounds (especially if groups that have traditionally
been the poorest and most vulnerable lose ground to
those with greater economic security); or (c) political
economy grounds (especially if the losers have the
capacity to organize and threaten either the sustainability of reform or survival of the government).
Careful consideration is required in the design of
compensatory schemes—to ensure appropriate targeting of intended beneficiaries and cost effectiveness,
and to avoid perverse or distortionary incentive
schemes that might compromise implementation of
the intended policy (see box 12). It is also important to
Element 8: Assessing risks
Upon laying out the broad parameters of possible
reform alternatives, it is important to consider the
risk that some of the assumptions underlying the
analysis may not be realized.31 This process may provide further insight into policy choice and design,
including sequencing. Risk analysis addresses the
issue of what could go wrong to prevent a policy
reform from delivering the intended poverty or social
Box 12. Labor Downsizing and the Design of Compensation Packages in Vietnam
tive severance packages, based on the characteristics of individual workers.
The issue of labor downsizing and the design of compensation
packages have been analyzed ex ante in the context of Vietnam
by Martin Rama (2001). Proposed reforms included a major
downsizing operation involving the liquidation, divestiture,
or restructuring of approximately 6,000 state-owned enterprises, resulting in unemployment of roughly 5 percent of the
Vietnamese labor force or 450,000 workers. In anticipation of
the massive layoffs a special compensation package was developed which amounted to two months of salary per year of service plus a substantial cash training allowance. This package
was a result of policy debates around simulations generated
by Rama using DOSE (Downsizing Options Simulation Exercise). The simulation computed “acceptance rates” for alterna-
The acceptance rate is defined as the fraction of the workers for
whom the separation package would exceed the present value
of the estimated loss from job separation. Rama found that a
formula based solely on earnings history had a consistently
higher acceptance rate for men, while women found a uniform
lump-sum compensation more attractive. Based on these simulations, the government of Vietnam picked a separation package that involved a sizeable lump-sum component in the form
of the training allowance in order to ensure that female workers would not be unduly penalized by the layoffs.
29
A User’s Guide to Poverty and Social Impact Analysis
well as risks arising out of behavioral responses to the
proposed reforms. Obvious examples are political
economy risks that may be latent but may become
more acute when interest groups perceive reforms as
a threat. Another example might be increased exposure to exogenous market conditions in the absence of
risk coping or insurance mechanisms to deal with
external competition or market failures. Risk assessment is based on the premise that risks become reality when assumptions turn out to be wrong. The
likelihood of an assumption being invalid is, therefore, another way of judging the extent of risk. The
first step is to identify the assumptions—implicit and
explicit—about what should and should not happen
in order for a policy to achieve its goals. The next step
is to make a judgment as to the likelihood that each
assumption will hold, and its importance to policy.
The more likely it is that an important assumption
will be invalid, the greater will be the need to alter the
policy. If assumptions are considered important but
more likely to be valid, there may be a need for a contingency plan. A variety of tools are available for risk
assessment. In particular, social risk assessment compares data and indicators from the World Development Indicators with external agencies to estimate the
likelihood and importance of risks to the reform program (see annex).
Sensitivity analysis is usually applied in the context
of quantitative economic models, and entails varying
the magnitude of certain key parameters to judge their
sensitivity to the model’s outcomes. Sensitivity analysis is especially important for parameters that are particularly uncertain (as may be the case where these are
based on estimates from other countries) or where
risks are known (for example, droughts in the Sahel).
One practical limitation of the approach is that it is
more often used to test sensitivity within a given
model than to assess alternate scenarios using different
models, which is not always feasible.32
Scenario analysis is a tool for helping decisionmakers consider how policy impacts might vary in different plausible scenarios. Scenarios are based on a range
of social, economic, political, or technological outcomes that drive change in the country. In this way,
unexpected risks can be highlighted, and contingency
plans made.33
impacts. By addressing these questions explicitly,
adjustments can be made to mitigate the risks (for
example, modifying the reform or introducing complementary measures).
Risk analysis can therefore help governments to
anticipate—and avoid—major unintended consequences. The analysis should consider risks to the
reform program as well as risks emanating from its
impacts. Part of the challenge is to identify explicitly in
the analysis the assumptions that must be valid for a
policy to have its intended impact. This is a difficult
task and underscores the need to make operating
assumptions explicit in monitoring the evolution of
the policy reform and its evolving impacts (see the section on monitoring and evaluation, below).
There are four main types of risk in PSIA:
■ Institutional risks. These include risks that assump-
tions made regarding institutional performance
were incorrect. This could be due, for example, to
market or institutional failures in existence where
none was assumed (such as asymmetric information or missing markets), or to the fact that key
organizations involved perform in unexpected ways.
■ Political economy risks. This includes the risk that
powerful interest groups may undermine reform
objectives by blocking implementation, capturing
benefits, or reversing reform actions.
■ Exogenous risks. These include risk of shocks to the
external environment such as a natural disaster or
regional economic crisis that might have a bearing
on the vulnerability of the poor.
■ Other country risks. These include the threat of an
increase in political instability or social tensions
that could undermine effective implementation.
There are three main methods available to conduct
a risk analysis: risk assessment, sensitivity analysis, and
scenario analysis. The first and third are discussed in
more detail in the annex.
Risk assessment is an approach for systematically
identifying risks, and their importance to the reform
at hand. It looks beyond vulnerability risks, which are
captured by the impact analysis, to include consideration of risks arising out of the sociopolitical and institutional context that could undermine the reform, as
30
Elements of Good Poverty and Social Impact Analysis
Element 9: Monitoring and evaluating
impacts
line data), during, and after the reform. The evaluation
problem is particularly challenging in the case of economy-wide policy reforms. As these reforms often apply
to whole sectors or economies (unlike projects, which
are restricted to a group or specific region), it is difficult to establish the counterfactual. Use of control
groups is possible only when the policy has been initially designed as a pilot or phased in so that those who
do not initially experience the reforms can serve as
controls. The particular challenges of ex-post evaluation for certain kinds of economy-wide reform require
particular foresight in setting up an evaluation framework ex ante. Given the challenges of ex-post evaluation and the need for more rapid feedback on the
evolution and impact of policy, PSIA implies a special
role for monitoring for purely practical purposes.
Although monitoring cannot attribute causality, it can
say something about whether, for whatever reasons,
assumptions are holding and expected impacts are
materializing. Monitoring can identify where “things
are going well or going wrong,” as well as where supplementary interventions or changes in policy may be
needed to ensure that the desired impacts materialize.
For example, reforms that affect service delivery may
benefit from participatory monitoring and evaluation
that provides feedback from intended beneficiaries on
quality of service delivery. Some methods for participatory M&E are described below.
When identifying and designing reform based on exante PSIA, it is important to consider setting up at an
early stage systems for monitoring, social accountability, and ex-post evaluation of the impacts. In doing so,
some specific concerns should be borne in mind in the
context of reform-specific PSIA. This section outlines
these issues.34
As noted above, good PSIA calls for monitoring
and evaluation (M&E), both to validate ex-ante
analyses and to influence the reformulation of policy.
Effective PSIA therefore implies a heavy demand on
data and information bases. In considering the information needs of PSIA, it is essential to build where
possible on existing systems of M&E. This should be
done with a view to developing a coherent national
poverty monitoring system that brings together information bases, indicators, mechanisms for linking
M&E and policy decisionmaking, and so forth. This is
another area where capacity building is an embedded
part of PSIA: the development or refinement of systems for monitoring, social accountability, and evaluation is most effective where it strengthens in-country
capacity.35
Monitoring involves tracking the progress of
processes and implementation (as measured by indicators on inputs, outputs, and outcomes) associated
with an intervention. This is done to ensure that
agreed targets are met and the policy is on track. Evaluation analyzes how and why observed changes in
indicators have occurred. Impact evaluation assesses
the extent to which a past intervention has contributed
to changes in outcomes or impacts for individuals,
groups, households, and institutions.
Choosing indicators for PSIA
Several key criteria may be used to choose relevant
indicators to monitor for PSIA. First, if impacts are
transmitted through specific channels (for example,
changes in producer prices, increases in sectoral
employment), these are obvious indicators to track.
Second, if the conceptual framework underpinning
the analysis hinges on specific assumptions (for
example, that traders or firms will enter with liberalization, that consumers or producers will substitute,
or even that certain elasticities will be of certain
magnitude), the validity of these assumptions holding over time can also be monitored. As discussed
above, tracing impacts through transmission channels and making all assumptions explicit in undertaking PSIA increases understanding of the
theoretical premises on which the program is based.
Particular characteristics of M&E in the context
of PSIA
M&E related to PSIA may be seen as a subset of a
national poverty monitoring system, and as having
several characteristics. It is focused on monitoring
impacts of specific policy reforms with a view to validating policy analysis or informing policy adjustment
during the course of implementation. This ideally
requires information on key indicators before (base31
A User’s Guide to Poverty and Social Impact Analysis
Effective monitoring facilitates good evaluation
In the context of M&E, the process of tracing
through the theory-based transmission channels
also enables one to identify potential intermediate
and process indicators that can be used to monitor
the implementation and outcomes of reform. Third,
given the importance of monitoring for adjusting
policy in “real time,” some indicators for PSIA (such
as prices) should be chosen so that they can be
tracked over a short time period (such as six
months). The purpose is to identify proxy or intermediate indicators for outcomes or impacts that will
gradually materialize. One way to do this is to trace
through the critical assumptions or “theory”
through which it is believed the reform will influence outcomes. Fourth, it is important to establish
indicators to monitor key risks to reform (see the
preceding section on risk assessment). These might
cover reform-specific risks (regarding transmission
mechanisms or institutions, for example) or broader
risks arising from the sociopolitical context (such as
the risk of elite capture). Fifth, when monitoring the
impacts of a reform, it is important to ensure that
impacts on gender or the environment are included,
especially when they are expected to be significant.
Finally, the choice of indicators can be informed by
the existing set of indicators already monitored in
the country, in the context of the existing national
poverty monitoring system or of regular government
reporting to its stakeholders. Building on existing
systems reduces costs and limits duplications.
In addition, indicators should satisfy a simple set of
basic technical criteria true for all monitoring indicators. The ideal indicator will be:
Understanding gained during the process of ex-ante
analysis in the course of PSIA and the identification of
indicators helps in designing a good evaluation.
Process evaluation is important to understanding the
“hows” and “whys” of policy reform. Process indicators are usually timely and not costly to collect. Tracing transmission mechanisms prior to the reform
helps in thinking through implicit assumptions and
highlights where potential constraints or risks may
arise. The process also helps to evaluate whether the
expected impact of the reform is borne out in practice.
Where it is not, more in-depth analysis to explain
divergence can be conducted. When results confirm
the assumptions, documenting the lessons learned can
help in the design of similar reforms elsewhere or in
the future.
The approach used in identifying indicators will
ideally encompass both open-ended and close-ended
methods, and as far as possible incorporate participatory methods. Open-ended methods examine the how
and why of policy reform, and in the case of participatory methods, promote ownership, accountability, and
transparency. Close-ended methods, on the other
hand, only touch on the how and why of changes, and
are primarily designed to assess the magnitude of
change.
M&E to promote social accountability and
transparency
Monitoring and evaluation can also be implemented
to promote social accountability during the process
of reform, thereby leading to increased ownership
and sustainability. There are various M&E tools available that if used appropriately can help to promote
social accountability. These include public expenditure tracking surveys (PETS), quantitative service
delivery surveys (QSDS), citizen report cards, and
participatory public expenditure reviews (see box
13). Similarly, perception surveys that capture more
qualitative information provide another means of
pinpointing problems within service provider organizations. Ideally, quantitative and perception surveys
can be used in tandem to provide critical information on the issues surrounding design and access to
policy reform.
■ Highly and unambiguously correlated with the
■
■
■
■
■
objective variable of interest (for instance, test
scores accurately reflect literacy)
Sensitive to changes in the outcome or impact of
interest
Timely, in that it can be collected in time to feed
back into policy adjustment
Relatively insensitive to other unrelated changes in
the sector
Relatively difficult to manipulate, either by target
groups or by policymakers
Not too costly to monitor.
32
Elements of Good Poverty and Social Impact Analysis
Box 13. M&E Tools for Promoting Accountability and Transparency
during Policy Reform
Public expenditure tracking surveys (PETS), quantitative service delivery surveys (QSDS), participatory public expenditure
reviews (PPER), and citizen report cards are useful tools for
tracking public expenditure and monitoring reform effectiveness as it pertains to the expected outcomes, processes, and
impacts that will occur as a result of policy reform.
work for a PPER in which civic groups influence stages of the
budget process in a cyclic and iterative manner. The PPER
framework can also be applied to the participatory monitoring
and evaluation of policy reforms covering all levels of indicators—input, output, outcome, impact—in a participatory
manner. The system has four key stages:
PETS and QSDS collect data through structured interviews
and documentation from service providers. While a PETS
traces money through an organization, a QSDS provides a
more robust analysis by pinpointing organizational weaknesses that can be addressed through reform. One output of
these survey instruments is a case-specific diagnosis of public
service delivery, helping to identify weaknesses in implementation capacity and suggesting where reform efforts should be
concentrated. Data from PETS and QSDS can help provide
answers to several kinds of questions, including:
■
■
■
■
■
■
■
How to strengthen the “voice” of service users
What kind of accountability mechanisms between different
levels of government can improve service delivery
How to regulate private providers.
Formulation: how expenditure proposals are made, to
which sectors, and in what amount
Analysis: review of the impact and implication of alternative policy proposals and allocations
Expenditure tracking: identification of elusive bureaucratic channels through which funds flow, bottlenecks in
the flow of resources, and other deficiencies of delivery
systems
Performance evaluation: direct feedback from citizens (for
example, report cards) on quality of, access to, and satisfaction with public services.
One-off engagement at any stage of the PPER cycle can be useful, but participatory public expenditure systems only deliver
when the feedback loop is institutionalized and space is given
to external voice at each stage. Achieving that level of institutionalization requires the commitment of significant resources
over the long term.
Drawing on a number of successful cases and tested models
from around the globe, the World Bank has developed a frame-
impact evaluation is data- and time-intensive relative to other forms of evaluation, and often can only
be implemented after the reform has already been
in place for some time. Therefore, the decision to
do impact evaluations should be based on a need to
fill knowledge gaps, or to apply lessons learned in
expanding reforms.
■ Involvement of national expertise in the implementation and setting up of an M&E system—relevant
ministries, the statistical office, the planning office,
private research agencies, universities, NGOs—not
only promotes ownership, but helps to build capacity for poverty analysis.
A few key principles should be borne in mind in
establishing an M&E system:
■ Participatory monitoring and evaluation can help
promote ownership of reform. It can be used to
identify output, outcome, process, and impact indicators that are meaningful to stakeholders. Reaching
agreement on key performance indicators can be
challenging, and is much better dealt with prior to
the reform. Agreement on standards to be achieved is
valuable both for policy managers and for affected
parties who are then more likely to accept the results
of monitoring reports and use this to improve policy.
In addition, follow-up public disclosure of information strengthens commitment to the reform.
■ Accountability can be promoted by employing specific data collection tools designed to allow beneficiaries to monitor inputs and outputs of the
reform, while also soliciting their views on the
effects of policy outcomes on their well-being.
■ Selectivity in choosing whether to conduct an
impact evaluation or not is important because
Planning and implementing M&E: activities related to
each stage of the PSIA
Where possible, monitoring and evaluation systems
for PSIA should be integrated within an existing
national poverty monitoring and evaluation system.
Building on existing resources reduces the cost of setting up the system, and further strengthens existing
national capacity.
33
A User’s Guide to Poverty and Social Impact Analysis
on a six-month-plus cycle, depending on the reform).
Soon after implementation begins, perhaps after three to
six months, preliminary monitoring and evaluation of
processes can be conducted to see whether the theory of
how the reform would work is being supported in practice. Do specified inputs and outputs appear to be leading to outcomes or impact in the manner expected? If
not, why not? At this time, midstream adjustments are
made, as required, to ensure the reform is on track.
In the post-reform or completion stage, roughly
three to six months after completion of the reform,
there could be—as a matter of good practice—a follow-up assessment and an incidence analysis of basic
outcome indicators to identify early “losers” and “winners” and reasons for the patterns observed. This
analysis, along with a more rigorous evaluation,
should ideally be repeated as required to fill knowledge
gaps in key policy areas, or to inform plans to further
deepen or expand reforms or scale up pilots.
Before the reform, while analysts are still grappling
with the key questions and objectives of the PSIA, a
preliminary list of indicators and required tasks and
timeline for the M&E system can be identified. In particular, it will be important to ascertain the existing
information base and gaps, including the availability
of relevant baseline data with regard to key indicators
and welfare measures and the possible need to collect
baseline data (see table 3).
Once some ex-ante analysis is completed and there
is an improved understanding of how the reform will
operate, the preliminary list of indicators, particularly
intermediate and proxy indicators, can be refined;
these may include views and perceptions of those to be
affected. An instrument can then be developed to be
used in measuring the indicators. It is important that
improved understanding of the program and indicators feed into the design of the quantitative evaluation.
Once indicators have been identified, plans can be
made to collect any missing baseline data, ideally
before implementation of the reform.
During the reform or implementation period, there
could be a periodic collection of indicators (proxy/intermediate, every three to six months; some indicators,
such as prices, every month; outcome/impact indicators
Element 10: Fostering policy debate
and feeding back into policy choice
For low-income countries, PSIA has been conceptualized as an integral part of the PRSP process and as an
Table 3. Planning M&E as Part of Poverty and Social Impact Analysis
Reform timeline
STAGE 1:
Prior to reform
PSIA timeline
M&E processes timeline
Identify key reform issues, questions, outcomes, and
risks for investigation.
Trace out “theory” of how reform will lead to the desired
results on the ground.
Identify input, output, intermediate, outcome, and impact indicators.
Identify availability of baseline data.
Identify existing information sources and gaps.
Specify required tasks/needs for covering gaps in M&E.
Preliminary field visit for ex-ante analysis.
Design ex-ante analysis.
Identify specific institution(s) to be responsible for M&E.
Begin to define process for M&E—periodicity for data collection; storage;
maintenance, etc.
Plan collection of baseline data, if such data do not exist.
Conduct ex-ante analysis.
Refine preliminary indicators with input from key stakeholders.
Collect baseline data.
Design instrument to be used in measuring indicators.
STAGE 2: During
implementation of
reform
3–6 months after initial implementation (and periodically
up until completion of reforms): follow-up analysis.
Process evaluation.
Social impact assessment.
Preliminary incidence analysis.
STAGE 3: Completion/post
implementation of reform
3–6 months to 1 year after completion of reforms
(depending on outcomes of interest).
Process evaluation.
Social impact assessment.
Incidence analysis.
34
Elements of Good Poverty and Social Impact Analysis
given policy reform. Such initiatives are particularly
relevant in the context of widespread uncertainty, suspicion, and ignorance—or in countries where poor
and marginalized groups have no political voice.
Establishing systems and forums for policy debate is
not only a valuable part of ex-ante PSIA, but is also
important for its contribution to monitoring and
social accountability during implementation of a
reform and ex post, as discussed above.
There may also be good reasons for a government
to take a policy forum seriously. Elected leaders who
rely on democratic legitimacy to bolster their popularity may find such a forum attractive, as may policymakers who are genuinely uncertain about which
policy reform path to take. From a leadership perspective, it may be sensible and more sustainable to pursue
a policy that rests on a social coalition or bargain than
one that theory may dictate as first-best.
Convening such policy forums among stakeholders,
however, is not without risks. One is that implicit conflict between major interests may become open hostility. A second risk is that political competition may
override the possibility of constructive dialogue.37 Yet
another common risk is that overly high expectations
of the forum will result in disappointment: people
may assume that public debate will lead to the adoption of a policy that simultaneously meets the needs of
all stakeholders, whereas in reality the typical process
of negotiation and compromise during policy formulation often leads to policies that do not mirror all
stakeholder preferences.
Managing the process of policy debate and discussion itself requires some planning, particularly in
order to manage risks. In particular, once the decision
is made to convene a forum, three concrete issues must
be addressed: whom to invite, what to discuss, and
how to structure the dialogue. These decisions are best
made jointly by the PSIA team and the relevant ministry or agency implementing the reform under consideration. In the context of social accountability
discussed above, the government may integrate these
debates within existing political processes (for example, by opening parliamentary debates to outside
stakeholders), but may also consider setting up more
inclusive, long-term structures of policy debate—such
as regular consultations, national workshops, or “town
element of the dialogue on the country’s poverty
reduction strategy. Fostering and drawing upon public
discussion of policy can be useful at various points of
the PSIA process —for example, to help identify stakeholders and their positions, to understand transmission channels, to validate technical impact analysis, or
to leverage social accountability. It is critical for PSIA
to “close the loop” and ensure that the lessons learned
from impact analysis, monitoring and evaluation,
social accountability, and public policy debate actually
inform and affect policy.36
Fostering policy debate
Policy formulation is not simply a technical process; it
is political as well. PSIA provides the technical parameters for evidence-based policymaking, laying out for
policymakers what is feasible and what are the likely
impacts of proposed policies and reform actions. The
accompanying debates determine what is likely to be
realistic in that political context, where the perceptions
and interests of particular constituencies are invariably weighed against the merits and demerits of the
reform. For that reason, the policy debate needs to
involve technocrats and researchers as well as parliamentarians, civil society, donors, and other key stakeholders whose support is essential to the reform.
The process of policy debate, including among
stakeholders, can be just as important as the analysis.
Numerous studies have concluded that policy is most
likely to be effective where there is broad ownership,
and policy debate among stakeholders is useful in
developing consensus and building ownership. One
way to approach this is to disseminate information
about the proposed reform and the results of the PSIA
to the public, especially to key stakeholders, and then
to organize a policy forum where stakeholders can discuss the tradeoffs involved. Such a policy forum can
produce invaluable information. Insights gained
through dialogue may be technical (for example, academic research) or social (for example, the perspectives and concerns of groups that typically do not
participate in the formal policy debate process). These
insights can either validate or revise previous hypotheses or analysis, including critical assumptions. Communicating policy impacts to stakeholders can also
enhance their understanding of the logic behind a
35
A User’s Guide to Poverty and Social Impact Analysis
hall meetings.”38 In many low-income countries, such
structures were established during the poverty reduction strategy (PRS) consultation process. Building on
those structures may be an easy and viable way to sustain this policy dialogue.
organizations (see GTZ 1991). For a description of the
problem tree see http://europa.eu.int/comm/europeaid/evaluation/methods/PCM_Manual_
EN-march.pdf, and European Commission 2002.
2. To the extent that stakeholder analysis helps
focus subsequent research on specific sets of actors, it
increases the relevance of more complex analysis of
poverty and social impacts while reducing time and
cost. A more detailed discussion of stakeholders and
their relevance to policies and programs is provided in
the Social Analysis Sourcebook (World Bank 2002c).
3. The identification process disaggregates these
actors in terms of social characteristics—such as cultural, structural, economic, political, or governmental.
4. Doing this early on provides a basis for early validation of hypotheses, subsequent identification of
data and information needs, and more rigorous analysis of hypotheses in subsequent steps of PSIA.
5. Some questions address issues of ownership and
commitment discussed in the previous section. In situations where informant interviews are not feasible or
where findings are not considered reliable, the institutional assessment tool can be used to conduct or complement stakeholder analysis.
6. Toolkits for institutional assessments can be found
at http://www1.worldbank.org/publicsector/toolkits.htm.
7. This statement requires qualifiers. First, faster
methods of close-ended data collection and analysis
are being developed (for example, the Core Welfare
Indicator Questionnaire). Second, reliable open-ended
analysis requires time and care if quality is to be
ensured.
8. These data collection instruments have often
been employed using non-random samples, for example in ethnographic analysis. However, there is no reason that they could not be used on random samples to
generate statistically representative data. Likewise,
non-numeric data could be coded into numeric data.
9. See Rao and Woolcock 2003 for examples of
mixed methods.
10. Most countries have now undertaken at least
one national household survey, although at times the
vintage and quality of data are an issue. Intra-household data, when available, can permit distributional
analysis at the level of individual household members,
a particular concern in considering the welfare of
Feeding back into policy choice
Ensuring that lessons learned from the continuous
monitoring and analysis of policy implementation
feed back to the redesign and adjustment of policy is a
major objective of PSIA. Sound ex-ante PSIA, as discussed, should lead to an explicit articulation of
expected impacts, transmission mechanisms, and
assumptions, and the establishment of a monitoring
system for key indicators tracking the evolution of the
reform program. Necessarily, ex-ante PSIA will not get
everything right. Rather, monitoring and evaluation,
during and after policy implementation, is a critical
part of PSIA, with the objectives of (a) correcting
flawed policies, (b) making adjustments to improve
policy choices, and/or (c) identifying constraints and
opportunities for further public action to maximize
poverty-reducing impacts.
A critical step in the PSIA loop, therefore, is the
feedback of lessons from the monitoring of reforms
during implementation and the subsequent evaluation
of the poverty and social impacts of policy choice, so
that the M&E can lead to appropriate adjustment of
policy. Institutional setups are fundamental here. A
common pitfall is that units or systems charged with
M&E are not properly linked with the decisionmaking
bodies responsible for policy formulation. The crucial
final link in an effective PSIA process, then, is ensuring
that the key body making decisions about a particular
policy reform is accountable for and charged with the
reporting of related M&E and the periodic reassessment of policy. Here again, building institutional
capacity, by creating such linkages where they may not
previously exist, is an important part of the PSIA
agenda.
Notes
1. The problem tree is a tool that has been popularized through its integration within the ZOPP methodology championed by many European development
36
Elements of Good Poverty and Social Impact Analysis
“tools to assess public service delivery,” later in this
section.
16. Behavioral impact analysis, in focusing on
demand analysis and supply analysis separately, can
arguably be seen as a “partial” equilibrium analysis.
The distinction drawn here is that since market
demand and supply are not equated and do not clear,
it is not technically “equilibrium” analysis.
17. In general equilibrium terms, it also effectively
assumes a closure.
18. The standard caution and caveat with respect to
economic modeling thus applies: great care should be
taken in specifying the model and its parameters to
country context and in making explicit the specific
assumptions and limitations of simulations derived
from such models.
19. Supply is either perfectly elastic (if chosen to be
endogenous) and entirely demand driven, or perfectly
inelastic—that is, supply is constant. SAM-IO simulations also vary greatly depending on the assumptions
made about which accounts are exogenous and which
endogenous.
20. Dervis, de Melo, and Robinson (1982) and
Shoven and Whalley (1992) provide good summaries.
21. It is also possible to run the micro-simulation
exercise not on the basis of parameters derived from a
consistent macro-model, but on the basis of exogenously assumed changes in parameters. Such an
approach would not be so different from the simplest
form of “direct impact analysis” described earlier.
22. This is also an area where work is still ongoing
and new tools and applications continue to be developed.
23. This has been done, for example, by Agénor
(2002), who has estimated such an equation—including the relevant elasticities—on the basis of a crosscountry regression tailored to take as inputs the
outputs of the RMSM-X model. One limitation to this
approach relates to the robustness of cross-country
estimates of these elasticities when applied to a
national context.
24. SimSIP has a module that looks at growth
impacts, and is being expanded to include a module
that will accept as inputs the key aggregate wage and
consumption variables generated from the 1-2-3
model.
women or other individuals who may be less powerful
or privileged within the household.
11. In examining an economy-wide reform, such as
a rice tariff increase, it would obviously be preferable
to adopt a representative sample for any new survey, or
to adopt the same sample (or select a panel) from a
household survey for which data already exist. Where
the reform is location-specific, or affects a specified
population—for example, with the shutting down or
privatization of a state mining company—a purposive
sample of those expected to be directly affected would
be appropriate.
12. Using a non-representative sample to extrapolate differentiated impacts of policies among groups
nationwide assumes that national distributional characteristics are identical to those of the non-representative sample—a non-trivial assumption.
13. For the purpose of presenting this simple table,
indicative classifications of high, medium, and low are
used, whereas clearly this is a continuum in practice.
While recognizing that time, data, and local capacity
are not perfectly correlated, they are deemed a close
enough match to collapse into a single dimension. For
example, when data is used as a proxy for this dimension, “low” means that no nationally representative
household survey data exist; “medium” means that
nationally representative household survey data exist;
and “high” indicates the existence of nationally representative household survey data along with other data,
such as census data for poverty mapping, national
accounts, and other data for computable general equilibrium models.
14. The Social Analysis Sourcebook (World Bank
2002c) provides a more detailed description linking
equity and social sustainability to development outcomes.
15. Incidence analysis has drawbacks. First, it does
not explain why things are the way they are. Second,
whereas incidence may use public expenditure as the
measure of the service’s benefit to the recipient, there
may be no correlation between expenditure and
received (or perceived) value, or outcomes. Third, as
with many interpersonal welfare comparisons, the
results of the analysis may vary depending on the
method and the dimension used to rank households.
See Demery 2000 and van de Walle 1998. See also
37
A User’s Guide to Poverty and Social Impact Analysis
nario analysis, see: www.gbn.org/public/gbnstory/
downloads/gbn_mont_fleur.pdf (South Africa).
34. This discussion deals with monitoring and evaluation only as they relate to PSIA. It is not intended as
a comprehensive treatment of the issue.
35. Building capacity in this context includes not
only the development of technical skills, but also
changes in incentives and demands for such information among country stakeholders (including government) as well as improved understanding of what
constitutes a good information base and how that
information base can be used for more creative analysis and for immediate policy decisionmaking.
36. This discussion deals with policy feedback only
as it relates to PSIA. It is not intended as a comprehensive treatment of the issue.
37. Under some circumstances, there may be compelling political reasons to avoid public forums. Examples of situations in which a policy dialogue may be
inadvisable are: (a) government commitment to the
policy is irreversible regardless of public reaction to
short-term costs; (b) an intransigent opposition party
or social movement is expected to use the forum simply as a vehicle to embarrass the government; (c) representatives of marginalized people are lacking,
meaning that the only organized interests likely to
have a seat at the table are privileged social groups; or
(d) open violence between participants is a serious
possibility. In such cases, some other form of consultation with stakeholders may be more appropriate than
a public policy forum.
38. This is another area where good PSIA should
consider capacity building as part of the agenda. At the
institutional level, capacity is required to organize
such forums and to open up space for policy discussion. At the individual level, capacity is often required
for informed and effective participation and thus for
an informed debate.
25. The separate labor and poverty module can
simulate the impact of policies on the labor market,
income and expenditures, and related social welfare
indicators. It permits the reallocation of labor in
response to changes in prices and wages.
26. Ianchovichina, Nicita, and Soloaga (2001) used
a similar approach to examine the impact of NAFTA
on household welfare in Mexico.
27. PSIA conducted for the water sector in Africa
has highlighted the importance of carefully evaluating
which mechanism is best suited to specific country
conditions (lifeline tariffs, coupon schemes, subsidized
types of supply). This is often done by consulting consumers and key stakeholders such as utility personnel.
28. Subsidization choice would depend, at least in
part, on institutional capacity and transaction costs of
delivering the subsidy.
29. It is worth noting that such exemptions may
introduce undesirable distortions into the tax and
incentive scheme, and not only from an efficiency
standpoint. To the extent that they allow non-poor
producers to avoid taxes legally or facilitate tax evasion, exemptions that appear patently progressive can
limit progressive budgets that address social programs
for the poor.
30. The opportunity cost calculation is complicated
to the extent that the reform package as a whole might
be conditional on the compensation mechanism.
31. This discussion deals with risk analysis only as it
relates to PSIA. It is not intended as a comprehensive
treatment of the issue. For further treatment of risks
see the Social Analysis Sourcebook (World Bank 2002c).
32. This is being done, for example, in Madagascar,
where three different modeling approaches are being
used to assess the impact of a rice tariff on distribution.
33. For an operational discussion with examples, see
Maack 2001. For in-depth case studies of applied sce-
38
4 Challenges and Operational Principles
line between simplifying reality to explain impacts
and capturing context-specific institutions and
behavior.
Second, the extent and nature of reform impacts
may differ over time. For instance, the impact of tax
reform might be limited to a single sector in the short
run but might expand to other sectors over time as
agents adjust to the new tax rates. Or a policy might
result in short-term losses and gains among different
groups, even when long-term effects are expected to
be positive. Capturing these inter-temporal dimensions within distributional analysis is a complex
undertaking.
Third, rigorous analysis requires a comparison to
be made between outcomes with and without reform
(the status quo itself being an alternative policy choice
or counterfactual). This is very hard to do ex ante,
when the analyst has to “simulate” what would happen
without a reform. It is also challenging in ex-post
analysis, as many factors will have changed during
reform implementation, masking reform-specific
effects.
Finally, addressing these analytical challenges
requires the right economic and social tools. Many
useful tools exist, and this User’s Guide highlights
some of the main ones. But more work is needed to
develop analytical methods that are better equipped to
meet the gaps and to develop more rigorous indicative
survey tools and analysis, where adequate information
is otherwise lacking.
The previous chapter has presented a road map to
conducting good PSIA. Practitioners should follow an
approach to PSIA that is country- and context-specific, dependent upon available data and capacity as
well as the reform issue in question. Key constraints
and principles are briefly outlined below.
Constraints
Specific challenges that analysts may expect in practice
include constraints on data, analysis, capacity, and time.
Data and information constraints
In many instances the data and information required
to do a comprehensive analysis are not readily available. Household survey data, which are particularly
relevant to undertaking distributional analysis on a
national level, sometimes do not exist or are dated.
Equally common, information sources that do exist,
including survey data and sociological analyses, may
not address questions relevant to the reform at hand.
Analytical constraints
First, it is difficult to analyze the impact of macroeconomic and structural reforms at the microeconomic or household level. Policies have many direct
and indirect effects at the microeconomic level,
mediated through local institutions and behavior.
It is often difficult to capture the complexity of
reality in a model. The analyst has to walk a fine
39
A User’s Guide to Poverty and Social Impact Analysis
Capacity constraints
Increase attention to ex-ante analysis
Capacity constraints affect the choice of analytical
method. In poor countries, capacity to analyze policy
is typically limited among government agencies, academia, and civil society organizations. So while rigorous analysis might call for complex tools and methods,
local capacity may be suited to simpler approaches.
Over time, however, the capacity of in-country development agencies also requires strengthening, in terms
of both core analytical expertise and resources allocated to PSIA.
It is important that ex-ante analysis of expected
poverty and social impacts underpin the design and
choice of policies, particularly those that are expected
to have the greatest impacts in the short to medium
term. This will help ensure that policies are conceived,
designed, and implemented with a view to enhancing
poverty reduction and social objectives.
Build on earlier experience
In practice, reforms often involve a series of measures
over a long period of time. The ex-ante analysis of
future reforms can be informed by the analysis of earlier reforms to ensure that past events and changes are
considered. Where possible, ex-post and ex-ante
analysis should be combined.
Time constraints
While the analyst may face difficult data and analytical
challenges, the policymaker is often under pressure to
make fast policy decisions and will not want to wait for
a rigorous PSIA to be completed. In such circumstances—such as in an economic crisis—arguments
for postponing policy decisions until there is adequate
analysis, debate, and consensus will need to be
weighed against the case for acting expediently. In
other cases, policymakers may want to time their
action for a particular moment in tune with the policy
or political cycle.
Use monitoring and evaluation to validate ex-ante
analysis
Ex-ante analysis cannot fully capture policy impacts. It
is therefore important to track actual results through
monitoring and, where possible, ex-post evaluation.
That way, midcourse corrections can be made to
reforms that are not having their intended poverty or
social impact. In many low-income countries, national
poverty monitoring capacity is being developed
through PRS monitoring systems. Whenever possible,
PSIA monitoring should be integrated within the PRS
monitoring system.
Principles
The challenges outlined above have often deterred policy analysts and decisionmakers from undertaking exante assessments of the poverty and social impacts of
reform. While some have argued that “no analysis is better than bad analysis,” it is important to consider what
analysis is feasible, even where data and capacity are
limited. The question, then, is how to approach poverty
and social impact analysis in the face of the various constraints. Some basic principles for a good analysis of
poverty and social impacts of reforms are as follows.
Maintain flexibility on tools and methods
It is important to tailor approaches to country capacity, reform issues, data availability, and time pressures.
In some circumstances, some basic economic analysis
enriched with qualitative analysis may be appropriate,
while in other cases econometric modeling may be the
most useful entry for PSIA. Understanding of impacts
is enhanced when results from different analytical
techniques reinforce each other or highlight different
aspects of impacts.
Promote country ownership
If PSIA is to be an effective tool for policy, it needs to
be country-owned. Ideally, countries should be
responsible for the choice of reforms and for the
analysis. In undertaking the analysis, they can seek
external assistance from partners including the World
Bank, the United Nations, and bilateral donors.
Increase transparency in the links between policy and
poverty
There is much to be gained from laying out for public
scrutiny the logic behind a policy choice—including
40
Challenges and Operational Principles
Build national capacity
expected losers and winners from reform, key assumptions, and transmission mechanisms. It can help promote national debate and acceptance of reform, and
serve as a baseline against which to monitor progress.
Moreover, it can highlight potential tradeoffs between
the long-run benefits of reform, in terms of higher
growth and poverty reduction, and possible welfare
declines in the short run.
Building national capacity is key to improving analytical rigor over time, in tandem with strengthened
country ownership. Many low-income countries
have limited capacity and experience in areas of critical importance to PSIA. These areas include data
collection systems, monitoring and evaluation systems, the capacity to conduct analysis and to translate data and analysis into policy, and the
institutional structures and mechanisms for debate
on such policy issues in the public domain. Building
national capacity in these areas must be a fundamental crosscutting aspect of PSIA. Development
partners, including the Bank, have an important role
in strengthening national capacity and in filling analytical gaps. PSIA approaches that foster “learning by
doing” should undergird development partners’
assistance to countries.
Strive to enhance gains and minimize losses,
especially among the poor
PSIA should give explicit consideration to such measures as alternative policy choices and complementary or
compensatory policies intended to enhance the benefits
to stakeholders, especially poor people, and minimize
the losses they may experience as a result of reform.
This will strengthen the pro-poor impact of policies
and improve their acceptability and sustainability.
41
5 Possible Summary Matrix
for PSIA (question 1) and the institutional mechanisms through which the reform will be carried out
(question 2). It then allows the analyst to summarize
the anticipated impact of the policy reform on different stakeholders, as transmitted through the five channels discussed in chapter 2: employment, prices, access
to goods and services, assets, and transfers and taxes
(questions 3, 4, and 5). The analyst should explicitly
recognize the stakeholders who are likely to gain from
the reform, those who are likely to lose, and those who
are likely to have significant influence over the reform.
The matrix also calls for an explicit statement of the
assumptions underlying the reform. Depending on the
country concerned, conclusions on likely policy
impacts will draw on differing information bases and
tools. For example, in one country context the matrix
may be filled out using informed reasoning based on
secondary data and qualitative field research; in
another context, the conclusions may be based on
empirically simulated effects derived from modeling
techniques using data from a recent household survey
and existing social analysis. In either instance, the
matrix calls for a description of the nature of the
information base and analytical methodology. The
matrix also calls for the analyst to specify key risks
associated with the reform, their likelihood and
expected magnitude (question 6). Finally, it proposes
to present the impact that the analysis has had on
national policy discussions (question 7).
Chapter 3 presented a series of elements for good
PSIA. Pulling these elements together in a coherent,
strategic, and integrative fashion is what makes for
good poverty and social impact analysis. Invariably, as
discussed throughout this paper, a sensible approach
to PSIA is going to be country- and context-specific,
dependent upon available data and capacity as well as
the reform issue in question. Box 14 provides an
example of this, describing the PSIA approach currently being used in Chad to address an ongoing
reform issue in that country.
The User’s Guide recognizes that the tools and techniques used for PSIA are likely to vary greatly across
countries and reforms. However, regardless of the chosen methodology, there are some key components that
should be addressed in this kind of analysis. Table 4
presents an example of a summary matrix that captures
and integrates these key components. In addition to
providing the analyst with a framework for considering
and articulating key aspects of PSIA for a given reform,
it offers a template for making explicit some of the
results and assumptions underlying such analysis. The
matrix itself can serve as a useful tool during the PSIA
process. For instance, an analyst may wish to sketch out
the priors in each of the 10 elements of good PSIA
before even undertaking an analysis, and then return to
the matrix to validate or correct these hypotheses.
The matrix calls for the analyst to set out the reform
components and the reasons for selecting that reform
42
Possible Summary Matrix
Box 14. Poverty and Social Impact Analysis of Cotton Reform in Chad
The aim of the scenario study is to identify and evaluate the
technical and economic efficiency of alternative scenarios for
privatizing Cotontchad. The study examines options for privatization (such as continued vertical integration, separate private ginneries, and so on) and assesses the risk posed by each.
Cotton is a key crop in Chad, both for revenue generation and
for poverty reduction. Cotton accounted for 24 percent of total
government revenues in 1997 and is the most important cash
earner for about 300,000 rural farm families. However, weak
organization and knowledge among farmers’ groups coupled
with structural inefficiencies in the sector have resulted in low
yields and low farmer revenues.
In parallel with this study, the PSIA assesses the impacts of the
reforms on the welfare of farmers in the sector. The ex-ante qualitative component identifies relevant stakeholders (including farmers, Cotontchad employees, microentrepreneurs), barriers faced by
stakeholders under different reform scenarios, the strength of current institutional structures, and the social risks of reform.
To address these inefficiencies, the government of Chad has
decided to privatize Cotontchad, the parastatal that currently
manages national cotton production, and strengthen farmers’
groups. A key objective of the cotton sector reforms is to
improve farmer incomes. Several factors underscore the government’s decision to proceed carefully in designing and
implementing the reforms: the possibility that yields will fall
further if reform prompts farmers to return to subsistence
agriculture, the limited availability of information on rural
poverty, and cotton farmers’ perceptions of the risks involved
with the reform. For these reasons, the government is carrying
out a poverty and social impact analysis to guide the reform.
The quantitative and qualitative analyses look at the compensation and enhancement measures necessary for reform success and highlight farmer capacity, access to credit, input use,
and transport. Further work involves a “quasi-comparison
group” for different types of farmers—those who produce cotton and those who do not or who have abandoned cotton—in
order to analyze the likely impact of the reforms on different
groups and get a sense of the welfare impact on farmers who
abandon cotton production.
In order to analyze the likely poverty and social impacts of
reform ex ante, the PSIA needs to do a problem analysis and
clarify the assumptions on which the program is based. The
PSIA team, in consultation with the government and local
counterparts, has identified pathways through which they
expect the reform to improve performance. By explaining the
causal links that tie program inputs to expected outputs, outcomes, and the ultimate goal of improving farmer income, the
team has explicitly outlined the assumptions for each transmission channel of the reform so that they can be verified.
The different scenarios for partial and complete privatization
and the ex-ante qualitative and quantitative work will be discussed during a stakeholder forum. This public discussion is
meant to increase the transparency of the reform and build
ownership by fostering policy debate.
In addition, there will be an ex-post impact evaluation of the
reform. The ex-ante analysis will define key indicators to be
monitored for policy feedback in ex-post analysis. The ex-post
analysis will employ quantitative methods of impact evaluation, which attempt as far as possible to assess impact based on
what would have happened in the absence of reforms. This expost quantitative analysis will be applied to a panel data set, to
estimate the impact on producer welfare.
The PSIA that grew out of these discussions has three components: (a) an economic scenario study of different options for
privatization; (b) ex-ante qualitative analysis, and a baseline
quantitative survey; and (c) ex-post analysis that includes both
qualitative and quantitative methods.
43
Possible support or opposition:
4. Through which channels are the stakeholders affected?
Labor
Prices
Access to
Assets
market
goods and
services
44
Transfers
and taxes
7. What impact has the analysis had on national policy discussions?
Other country risks:
Institutional risks:
Exogenous shocks:
Political economy risks:
Type/nature of risk
Likelihood
Expected magnitude
6. What are the main risks that would change the expected impact of the reform? What are their likelihood and expected magnitude?
What information basis and techniques were used to answer questions 3, 4, and 5?
Stakeholders with significant influence over the reform:
Stakeholders affected (positively and negatively):
3. Which stakeholders are likely to be affected
by the reforms? Which stakeholders are likely
to affect the reform and how?
2. What are the institutional mechanisms through which the reform will be carried out?
1. What reform was chosen (major components), and why?
Reform:
Table 4. A Summary Matrix for Poverty and Social Impact Analysis of Reform
5. What are the expected direction and order of magnitude of impact(s)? What
are the underlying assumptions?
A User’s Guide to Poverty and Social Impact Analysis
6 Conclusions
approaching PSIA. Furthermore, it has given a brief
overview of some of the tools and methods that
might be used in undertaking analysis of poverty
and social concerns associated with policy change. In
so doing, it has attempted to draw upon tools used
by economists and social scientists and to present
them in an integrated fashion. Applying these tools
to the operational context using this multidisciplinary approach will lead to a richer, more integrated
understanding of policy impacts. Moreover, because
of the marked differences between individual cases
in terms of reform issues, transmission channels,
and available data, the choice of tools and methods
used for PSIA will vary substantially by type of
reform.
This User’s Guide to PSIA has provided an initial
overview of the key considerations for practitioners
contemplating the poverty and social impacts of policy
options with a view to informing policy choice and
design. It contends that ex-ante analysis of the likely
poverty and social impacts of a specific reform can be
undertaken more systematically than is typically done
at present. It also takes the practical view that, for this to
be possible, approaches and methods will need to be
adapted to fit the context and circumstances at hand,
and the limits to what is possible through ex-ante analysis will need to be addressed through continual monitoring, analysis, and reevaluation of policy over time.
This User’s Guide has laid out 10 key elements to
be considered by analysts and policymakers in
45
Annex: Economic and Social Tools for Poverty
and Social Impact Analysis
evaluate, what types of questions it can answer, and its
complementarity with other tools/methods, (2) its
key elements, (3) the requirements in terms of data,
time, skills, software, and cost, (4) the limitations of
the tool/method, and (5) references and country
applications.
Note that some of the tools presented in this annex
belong to more than one category. For instance, beneficiary assessment or participatory poverty assessment
can also be used as monitoring tools; while public
expenditure tracking or quantitative service delivery
surveys can also be used to analyze stakeholders and
impacts. Also, note that some of the tools to analyze
impacts categorized under “social” or “economic”
actually use a mix of methods, as is the case for
demand analysis. Moreover, some of the techniques
presented can be used in carrying out more than one
type of analysis. For instance, demand and supply
analyses are components of partial equilibrium analysis presented under “Multi-market models”, and both
IMMPA and the Augmented CGE model with representative household approach also fall within the “general equilibrium models” category.
This annex presents information on a series of tools
and methods available for the analysis of poverty
and social impacts of reforms. This annex presents
summary information on the tools, drawing in particular on the Toolkit for Evaluating the Poverty and
Distributional Impact of Economic Policies and the
Social Analysis Sourcebook, which provide more
detailed information1. Additional guidance is currently under preparation on selected social and economic tools. The World Bank is also developing
guidance on issues, challenges, and tools that may be
of particular relevance in analyzing specific reforms.
A summary matrix and reform-specific notes will be
posted on an ongoing basis on the PSIA website.
This annex highlights some of the key tools for such
analysis, but does not aim to be comprehensive in its
coverage; updates on additional tools and methods
will be posted on an ongoing basis on the PSIA website: http://www.worldbank.org/psia. These tools are
organized following the User’s Guide elements,
including stakeholder analysis, institutional assessment, impact analysis, risk assessment and monitoring. In practice, the analysis of poverty and social
impacts of reforms requires the combination of a variety of complementary tools, both within and across
categories. In addition, some tools have evolved to
comprise the integrated application of both social and
economic methods.
Each tool or method is presented within a summary table. The table contains five components: (1)
what the tool/method is, what policy reforms it can
Note
1. These are available at
http://www.worldbank.org/psia and
http://www.worldbank.org/socialanalysis
sourcebook/ , respectively.
47
A User’s Guide to Poverty and Social Impact Analysis
The tools and methods presented in the annex include the following.
I. Identifying Stakeholders
– Stakeholder analysis
II. Assessing Institutions
– Institutional analysis
III.Analyzing Impacts – Social Tools
– Social impact analysis
– Beneficiary assessment
– Participatory poverty assessment
– Social capital assessment tool
– Demand analysis: Consumer assessment
IV. Analyzing Impacts – Economics Tools
1. Direct impact analysis tools
– Benefit incidence analysis (Average and Marginal)
– Tax incidence analysis
– Poverty mapping
2. Behavioral models.
– Ex-post behavioral marginal incidence analysis of public spending and social programs
– Ex-ante behavioral marginal evaluation of policy reforms
– Ex-post impact evaluation methods for assigned programs
– Demand analysis: Estimating demand functions
– Supply analysis
– Household models
3. Partial equilibrium models
– Partial equilibrium analysis: Multi-market models
– Partial equilibrium analysis: Reduced-form estimation
4. General equilibrium models
– Social Accounting Matrices
– Computable General Equilibrium (CGE) Models
5. Tools linking microeconomic distribution or behavior to macroeconomic frameworks or models
– PovStat
– SimSip Poverty
– 123 PRSP
– Poverty Analysis Macroeconomic Simulator (PAMS)
– Integrated macroeconomic model for poverty analysis (IMMPA)
– Augmented CGE Model with Representative Household Approach
V. Assessing Risks
– Social Risk Assessment
– Scenario Analysis
VI. Monitoring and Evaluation
– Public expenditure Tracking Survey (PETS)
– Quantitative Service Delivery Survey (QSDS)
– Citizen Report Card
– Community Score Card
48
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Stakeholder Analysis
What is it?
Stakeholder analysis is a systematic methodology that uses qualitative data to determine the interests and influence of different groups in
relation to a reform.
What can it be used for?
While stakeholder analysis can be carried out for any type of reform, it is particularly amenable to structural and sectoral reforms. Basic
stakeholder analysis should precede reform design and should be consistently deepened as reform elements are finalized.
What does it tell you?
Stakeholder analysis assesses: (i) the extent to which reform may provoke political or social action; (ii) the level of ownership among
different groups; (iii) differences in perception of the reform among different ethnic, religious or linguistic groups. Stakeholder analysis
can be expanded into fuller political economy analysis that identifies affected groups and looks at: (i) their position vis-à-vis policy; (ii)
their influence on government; (iii) the likelihood of their participation in coalitions to support change; (iv) strategies for overcoming
opposition such as compensating losers or delaying implementation.
Complementary tools:
• Normally used in conjunction with social impact analysis.
• Stakeholder analysis identifies groups to consider as categories for analysis. It is useful for the design of household surveys,
microeconomic modeling and micro-macro linked models.
Key Elements:
Stakeholder analysis is iterative, and usually proceeds through the following sources of data to reach final conclusions: (i) background
information on constraints to effective government policy- making; (ii) key informant interviews that identify specific stakeholders
relevant to the sustainability of policy reform. Participants should be drawn from a diverse groups of interests in order to limit bias; (iii)
verification of assumptions about stakeholder influence and interest through survey work and quantitative analysis of secondary data
Requirements
Data/information:
Stakeholder interests are seldom explicitly spelled out in existing sources. The main sources of information are: (I) key informant
interviews; (ii) secondary material such as newspaper articles, and social science research.
Time:
In cases where key informant interviews are already being carried out as part of other qualitative analysis, preparing an analytical piece
on stakeholders can take as little as one additional staff week of effort. In cases where there is no significant qualitative work planned,
a thorough exercise would likely involved a trip to the field and two to three staff weeks of effort.
Analysis that is meant to predict the positions of key stakeholders in different reform scenarios is not a one-off piece of work and should
grow out of the findings of other analytic work. Ensuring a complete and updated picture may require that specialists carry out the work
over several calendar months.
Skills:
Sociological or anthropological training is helpful, as is a background in political science. Local knowledge, including contacts with local
experts, is crucial. Those carrying out the analysis must also thoroughly understand the reform and the recent history in the sector.
Supporting software: N/A
Financial cost:
When combined with other qualitative work, the incremental cost of stakeholder analysis can be as low as US$10,000. When no
qualitative work is planned, costs can be up to US$25,000.
Limitations:
Stakeholder analysis relies on qualitative data and perceptions and preferences. The absence of statistical representative places greater
onus on careful selection of respondents and interpretation of data.
References and applications:
• Bianchi and Kossoudji. (2001).
• Brinkerhoff and Crosby (2002).
• World Bank (2002e), Annex VII on Guyana.
49
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Institutional Analysis
What is it?
An analytical approach that uses qualitative methods to unpack the “black box” of decision-making and implementation processes
What can it be used for?
Useful for PSIA regardless of reform type, but particularly important for policy changes involving institutional reforms, such as
decentralization of public services, utility reforms, land reforms, social safety net reforms. Useful for policy design and implementation.
What does it tell you?
Understanding of political economy and governance issues through analysis of the institutions that are involved in the design and
implementation of reforms, and identification of dynamic processes, and potential constraints in this respect.
Steps include: (i) Identification of government agencies, non-government organizations and firms that carry out the policy reform. (ii)
Identification of their characteristics and dynamic relationships. Output: Understanding of the formal “rules of the game” (via static
mapping, i.e.: functional organigram), and the informal rules that govern actual behavior in decision-making processes (via process
mapping of crucial resource flows, e.g. money, information).
Complementary tools:
• Used in conjunction with Stakeholder Analysis, SIA, and demand analysis/customer assessments
• PETS, Benefit Incidence Analysis
Key Elements:
Three types of information: (i) background information on key stakeholders, and organizational structures of relevant agencies; and (ii)
in-depth interviews or focus groups with key informants from government agencies, non-government organizations and firms; (iii)
triangulation and cross-referencing with other information to validate background information and key informant interviews.
Requirements
Data/information:
Secondary material, including PERs, DPRs, IGRs, social/ political science research and in-country assessments of organizational structures
and institutional settings. Primary data, that illustrates informal practices and identifies the dynamic processes within the policy design
and implementation
Time:
A few weeks (4-5 person weeks) to a few months (2-3 person months for fieldwork, analysis and report): Can be completed quickly in
combination with a Stakeholder Analysis to gain a brief overview of the formal and informal rules of the game. Institutional Analysis that
aims to identify the dynamic processes within the policy design and implementation requires a more in-depth analysis, and may take a
few months.
Skills:
Sociological, anthropological, and public policy training (incl. qualitative field research skills) are helpful. In depth knowledge of the
country-context, reform area, policy design and implementation, and political economy (including interests and influences of key stake
holders) is crucial.
Supporting software: IPS Ltd.: http://www.ips-uk.com/ProcessMapping.htm - ProcessMap;
Toolpack.com: http://www.toolpack.com/workflow.html;
HPS Inc.: http://www.hps-inc.com/ithinkDemo.htm#;
Triaster http://www.processnavigator.com/english/index.html;
Ash House: http://www.ashhouse.co.uk/process.htm;
Process Mapping: http://www.processmapping.com/;
TSQ Europe: http://www.tqseurope.com/activemo.htm;
Designtech: http://www.designtech.com/processmap.html;
Financial cost:
Depending on the depth of analysis, it can be low-cost if used in combination with Stakeholder Analysis, or adapted to SIA
(US$ 25,000), but can be more costly if done more in depth (US$ 50,000).
Limitations:
Care should be taken in generalizing findings across different units of analysis and across regions with dissimilar informal institutions even
within a country (e.g. panchayat institutions will vary enormously across different states within India). Resource and time requirements
vary by the depth of analysis (incl. scope of geographical fieldwork done at local, provincial and/or national level) and reform complexity
which may necessitate continuing the analysis during implementation.
References and applications:
•
•
•
•
Brinkerhoff and Crosby (2002).
Hunt (1996).
North (1990).
Tymons and Jacobs (1997).
50
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Social Impact Analysis
What is it?
An analytical framework to identify the range of social impacts and responses to reform by people and institutions, including those that
are vulnerable or poor. Often undertaken in an iterative manner, and includes relatively detailed information on social context for reform.
What can it be used for?
Can be used for many types of policy reforms. Has been used extensively for mining sector restructuring, parastatal privatization and
agricultural reforms giving rise to significant social impacts.
What does it tell you?
Social, political context for reform, who is affected by the reform at what point in time, preferences and priorities of those affected by
reform, constraints to implementation of reform, how people, institutions are likely to respond to reform including whether assumptions
on how they will react or be affected by the reform are correct. Also provides insight into coping mechanisms and social risks, suggestion
from stakeholders on most appropriate means to mitigate negative impact of reform and potential effectiveness in local context.
Complementary tools:
Used in conjunction with stakeholder analysis. Other tools such as institutional analysis and risk analysis complement and draw heavily
on SIA. SIA can feed into assumptions for economic modeling.
Key Elements:
Characterized by use of mixed methods and direct consultation of those potentially affected that can include a wide range of data
collection techniques: open-ended community discussion, key informant interviews, focus groups, quantitative survey, observation,
ethnographic field research, PRA. Proper structuring of qualitative methods and interpretation of both qualitative and quantitative research
requires sufficient knowledge of local customs and cultures and thus normally requires partnership with local consulting, NGO or research
firms. Typically, SIA uses purposive surveys to collect quantitative information from a sample representative of a particular region or
population groups relevant to a particular reform. This is particularly useful in situations when national household data do not exist or do
not contain the specific information needed to assess reform impacts.
Requirements
Data/information:
(1) The degree of diversity of the groups likely to be affected or to influence a reform (from the stakeholder analysis) based in part on
detailed country level contextual information (cultural, ethnic, regulatory and institutional issues relevant to the reform or affected
groups), typically from existing studies, press reports, and key informant interviews. This determines the sampling strategy for fieldwork.
(2) Direct data on stakeholder perspectives, typically from field research. (3) Quantitative data typically on income, expenditures,
behavioral responses, coping mechanisms or other variables relevant to the reform to compare with results from qualitative data.
Typically, SIA uses purposes surveys to obtain quantitative information relevant to a particular reform expected to have disproportionate
impacts on a specific region or known population groups. The sample will then be representative of that region but not nationally
representative. This is particularly useful for situations when national household data do not exist or do not contain the specific
information needed.
Time:
SIAs can vary greatly in length depending on the scale of research and the number of sample areas (which will be in part a function of
the diversity or complexity of the groups involved and the size of the population affected). As this is typically combined with stakeholder
analysis, a minimal time for both exercises is approximately 3 man months.
Skills:
Often requires either a team with mixed skills (in qualitative techniques and in quantitative data collection and analysis, and preferably
with someone with sector knowledge), or two teams or individuals working together. The coordination, and iterative analysis of both
qualitative or participatory data collection methods and quantitative analysis is paramount.
Supporting software: N/A
Financial cost:
Varies according on the depth and purpose of analysis. A complete mixed methods SIA costs US$80-100,000. When national household
survey data exist, a supplementary SIA for a specific reform would cost an average of US$35,000, excluding supervision time. May cost
more where local capacity is low and needs to be supplemented by international consultants.
Limitations:
SIA is not the best instrument to use for broad based reforms where the transmission channels and groups affected are not well known.
References and applications:
•
•
•
•
•
Finterbusch, Ingersoll and Llewellyn (1990).
Goldman (2000); Becker (1997).
World Bank (2002c) http://www.worldbank.org/socialanalysissourcebook/socialassess.htm.
Cernea and Kudat (1997) on the application to sectoral policy reforms including tariff issues.
Other applications: http://lnweb18.worldbank.org/ESSD/essdext.nsf/61ByDocName/CaseStudies
51
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Beneficiary Assessment
What is it?
A participatory assessment method and monitoring tool that incorporates direct consultation of those affected by and influencing reform.
Similar to PPA, it relies primarily on qualitative research though with less emphasis on the use of visual techniques and on community
follow-up to the research process.
What can it be used for?
Has traditionally been used to evaluate projects or sectoral reforms in the health, education, infrastructure, social protection and
agricultural sectors, but can be adapted to assess or monitor the impact of some discrete policy interventions where transmission channels
and affected groups are clearly defined. Can be used even for countries with limited capacity as an add-on to other economic tools.
Used both to evaluate proposed reforms, to signal constraints to participation faced by target group, as well as to gain beneficiary
feedback for ongoing reforms.
What does it tell you?
What is the beneficiary perspective on the problem being addressed by the reform, their perception of the proposed policy, and of any
mitigatory measures being considered. Provides insights into the likely reception the reform will receive, as well as issues that may arise
during implementation. Tends to reach down to the community-level, but not focused exclusively on the poor or the community.
Complementary tools:
• Used in conjunction with stakeholder analysis, and institutional analysis. Can also complement representative quantitative surveys.
• Information on how different groups are likely to react to a proposed policy change can influence assumptions in macro and micro
models, in terms of behavioral response (particularly where historical data is insufficient or lacking).
Key Elements:
Relies primarily on three data collection methods: (1) conversational interviews (2) focus group discussions, which in some cases have
been combined with PRA tools; and (3) direct and participant observation. Although information collected may be qualitative in nature,
also includes quantitative analysis of this beneficiary feedback.
Requirements
Data/information:
Background information on stakeholders, on cultural, ethnic, or socioeconomic variations, and on the variables determining whether
specific groups would be affected (such as type of access) is required to properly design a BA and its sampling strategy.
Time:
Generally within three to four months, from design to presentation of the final report.
Skills:
Sociological or anthropological training are helpful, but good listening skills are paramount. Good knowledge of the program, historical
and cultural setting also important.
Supporting software: N/A
Financial cost:
Average of US$40,000.
Limitations:
Tends to have a narrower focus than SIA or PPA, providing less contextual and historical background information, though also likely less
resource intensive.
References and applications:
• Salmen (2002).
• Salmen and Amelga (1998).
• For summaries of specific country application of both BA and PPAs see:
• http://www.worldbank.org/participation/beneficiaryassesment/beneficiary assessment.pdf
52
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Participatory Poverty Assessment
What is it?
An instrument for including the poor directly in discussions and debates on policies and priorities, and that relies primarily on qualitative,
visual, participatory rural appraisal techniques. Uses data collection techniques similar to BA, though with a greater focus on consultation
of the poor, and on a broader set of policy issues affecting the poor.
What can it be used for?
Can be adapted to the analysis or monitoring of many policy reforms. Has been used extensively in public expenditure reforms that
require priority setting, or better understanding the reasons for accountability or low service use, or for institutionally complex reforms
(such as land reform, liberalization of markets, labor market reforms) or for better targeting safety nets. Could also be used to monitor
the local impact of macroeconomic policies such as devaluation.
What does it tell you?
In-depth analysis of the views of the poor and their political, social, and institutional context; policy priorities of the poor,
multi-dimensional dynamic of poverty and of coping mechanisms; identification of constraints that could be overcome through public
action to increase access to reform benefits, with a particular focus on constraints for the poor.
Complementary tools:
• Used in conjunction with stakeholder analysis.
• Can also be used to complement institutional analysis, larger representative household surveys, or SOCAT.
• Can be used together with poverty mapping, statistical analysis of household surveys, public expenditure tracking surveys, and
benefit incidence analysis.
Key Elements:
PPAs (i) use a variety of flexible participatory methods that combine visual methods (mapping, matrices, diagrams) and verbal
techniques (open-ended interviews, discussion groups) and (ii) emphasize exercises that facilitate information sharing, analysis and
action, with a goal of giving communities more control over the research process.
By their very nature, PPAs may create opportunities or expectations of follow-up at the community level, such as the development of
community action plans, often supported by local government or NGOs.
Requirements
Data/information:
Selecting the appropriate (purposive) sample areas for PPAs (typically from 40 - 60 sample communities) requires an adequate
understanding of social, economic and poverty context of the various regions or areas of a country. PPAs focus on direct field research
and therefore do not have other information pre-requisites.
Time:
From 5 to 9 months for research and analysis, assuming a research team of between 10 and 20 people.
Skills:
Skilled and experienced facilitators, who are able to listen and record information in as unbiased a manner as possible, and to manage
expectations from the PPA at the community level.
Supporting software: N/A
Financial cost:
From US$15,000 to US$200,000 depending on scale.
Limitations:
Not statistically representative. May raise expectations for follow-up or service improvements at the community level that local actors
and/or the research team may not be able to provide.
References and applications:
•
•
•
•
•
Robb (2002).
Norton et al (2001).
Salmen (1995).
For summaries of specific country application of both BA and PPAs:
http://www.worldbank.org/participation/beneficiaryassesment/beneficiary assessment.pdf
53
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Social Capital Assessment Tool (SOCAT)
What is it?
A set of integrated quantitative and qualitative measurement tools to investigate institutions, networks and norms that enable collective
action. Has to be adapted to a specific research issue. Can be implemented in conjunction with other tools.
What can it be used for?
Primarily useful for reforms with low/medium indirect impacts. Agricultural reforms (changing subsidies/taxes), liberalizing markets,
financial reforms (changing access to credit), labor market reforms (active labor market programs), utility reforms (access to services),
decentralization, social safety net programs (changing public/private transfers).
What does it tell you?
Existence of institutions and networks affected by and/or involved in reform implementation. Which norms and values lead to policy
adoption or resistance? The distribution of social assets and their role in income generation and risk management. What are the impacts
of reforms on households with low social assets? Which adaptations in policy formulation and / or which mitigation measures are
advisable?
Complementary tools:
Stakeholder analysis, institutional analysis, social impact analysis (SIA), beneficiary assessments (BA).
Key Elements:
Integrated application and analysis of quantitative and qualitative information (surveys, key informant interviews, focus groups) obtained
at the level of households, communities, and organizations. Analysis builds on the understanding of solidarity, trust and cooperation, and
conflict resolution (cognitive social capital), as well as organizations and their membership (structural social capital).
Requirements
Data/information:
Use as standalone tool for social capital analysis, or use in conjunction with other surveys (e.g. LSMS, income/expenditure surveys) for
analysis of links between poverty and social capital. Modules for integration in other surveys are available, so are sector specific
questionnaires.
Time:
Typical application requires 3-4 months.
Skills:
Sociological or anthropological training are helpful, in particular a sound understanding of formal and informal institutions and networks.
Good knowledge of the program and its setting is crucial.
Supporting software: SOCAT Toolkit including interactive CD-ROM is available.
Financial cost:
Depends on sample size and local wage and transport costs for field team. Typical range for standalone SOCAT exercise would be
US$50,000 to US$200,000. Costs can be substantially lower if used in conjunction with other data collection instruments.
Limitations:
Collects social capital data only at micro and meso levels. For analysis of links between social capital and poverty, combination with other
survey is necessary.
References and applications:
• Grootaert and van Bastelaer (2002).
• Additional information at: http://poverty.worldbank.org/library/topic.php?topic=4294 or at:
http://iris.umd.edu/adass/proj/soccap.asp
54
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Demand Analysis: Consumer Assessment
What is it?
The adaptation and expansion of traditional demand analysis to a broader qualitative and quantitative research process that looks at
consumer or client demand for different types of services (willingness to pay, ability to pay, preferences), probes qualitative and other
factors driving demand and potential substitutes, obtains feedback on likely responses to potential changes tariffs or in service
management (such as privatization), and explores ways in which to more effectively help the poor in terms of price and access based on
local institutional context and past experience with programs targeted at the poor. (See also Table on Demand Analysis: Estimating
Demand Functions)
What can it be used for?
Has been used in energy sector reforms and water sector reforms including privatization, but can also be applied to changes in cost
recovery in other sectors such as health, education, or transport.
What does it tell you?
To shed light on how price increases affect different groups of consumers including the poor, specifically taking into account institutional
factors that affect the transmission of these prices. Also, Consumer Assessment (CA) helps to project more realistic revenue/cost
recovery levels, incorporate client perspectives and levels of satisfaction, and rank the service in question in terms of overall development
priorities of different groups of clients. In its application in Africa CA has also outlined the viability of various options for reaching the poor
given existing institutional and market constraints, and given their preferences.
Complementary tools:
• Can be used in conjunction with stakeholder analysis and institutional analysis. Elements of SOCAT can be integrated into CA. Can
also complement nationally representative household surveys.
• Feedback from CA can inform assumptions on elasticity or welfare impact on different groups in other economic models. In ECA, CA
has been used to build standard demand models as well.
Key Elements:
Requires: (1) quantitative household surveys that include, but are not limited, to willingness and ability to pay, indicators of vulnerability
or poverty, income, social capital and/or (2) can use existing LSMS or other household surveys and data from other utility or service
providers on types of consumers and consumption or service levels; and (3) traditional focus group discussions, or focus group
discussions using a variety of PRA (SARAR) visual aids. In some cases CA has also included (4) key informant interviews and (5)
observation to triangulate information obtained from the various sources. In Africa CA has also been integrated into utilities’ financial
models to project realistic cost recovery rates and tariffs.
Requirements
Data/information:
Data on sources and services for different groups of consumers, coverage levels, consumption levels and tariffs, over time if available,
from either utility data or direct research or existing surveys, and income distribution data by service type or customer grouping (though
this is often collected during the research). Most effective as a decision tool if actual and projected costs of service provision under
different scenarios are used in willingness to pay questions.
Time:
For CA generally six to eight months, with field work of two to three months total, though more disaggregated demand analysis (within
peri-urban areas of a city) has taken longer.
Skills:
Requires quantitative skills (economist, social economist, or sector economist) in addition to skills in qualitative research (sociologist,
anthropologist). Good knowledge of sector structure is essential.
Supporting software: N/A
Financial cost:
For fieldwork from US$40,000 up to over US$100,000 excluding supervision of consultants.
Limitations:
Requires skill in triangulating information to provide assessment of client response to changes in tariff levels, and to distinguish potential
biases in information provided. Also, effective qualitative work requires skilled facilitators. Willingness to pay questions can raise
expectations of service improvements, and need therefore to be carefully linked to sector constraints and likely scenarios.
References and applications:
• Lampietti et al (2001) on utility pricing in Armenia
• Sechaba Consultants (2002) on the water sector
55
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Benefit Incidence Analysis (Average and Marginal)
What is it?
Benefit incidence analysis estimates the impact of public transfers, taxes, subsidies, or policy changes that affect prices. BIA measures the
distributional incidence of benefits for different groups of interest, for instance households at different income levels or in different
regions. Average (or simple) BIA measures the incidence of all benefits - i.e. of the aggregate benefit. Marginal BIA estimates the
incidence of the last (or the next) unit of benefit. (See also Table on Tax Incidence Analysis)
What can it be used for?
Benefit incidence analysis is most commonly used to examine the impact of public expenditures and public expenditure reforms. It is also
applicable to other policy reforms, including reforms affecting prices that change household income or expenditure and tax reforms. It can
be applied to direct transfers as well as to transfers obtained by consuming subsidized goods or services.
What does it tell you?
Benefit incidence tells us who benefits from services, transfers, or price changes. When estimating the size of benefits received by
different groups, average BIA calculates the benefits received on average (i.e. on the basis of average unit costs); marginal BIA tells you
who will benefit from a increase or decrease in benefit (i.e. the marginal change). These two might be very different – typically,
additional beneficiaries are more likely to belong to groups not yet covered by the system (e.g. remote areas).
Complementary tools:
Simple or marginal BIA can be combined with information on household or individual behavior – see Tables on Behavioral Benefit
Incidence Analysis, Social Impact Analysis and Beneficiary Assessment. These techniques explain distributional changes from a policy
reform by taking into account the reactions households or individuals will have to the change.
Key Elements:
BIA proceeds as follows: (1) estimation of the value of the benefit: typically estimated as the cost of providing the service, transfer or
subsidy. This can be quite difficult, with issues related to the inclusion of investment and administrative costs, and the treatment of cost
recovery. Estimations are sometimes made at a regional level, to account for cost differences; (2) Identification of the users on the basis
of household surveys; (3) Aggregation of users into groups of interest (commonly defined by income levels, region, urban/rural location,
poor/non-poor, occupation, ethnicity, etc); (4) Accounting for household spending, in case of out-of-pocket expenditures to access the
benefit. In case of financial transfers, the income groups can be defined pre- or post-transfers, which will yield different results.
Requirements
Data/information:
(1) individual or household-level data from household surveys on welfare and on the use of service and receipt of public spending and
(2) information on public expenditure to estimate the value of the benefits. For marginal BIA, panel data is ideal, although methods exist
for cross-sectional data.
Time:
Analyzing household survey data can be time consuming, depending on how clean the data are, and how well managed the data entry
process was. BIA can take between 4 to 8 weeks depending on the condition of the household survey data, and the accessibility of the
unit cost of providing those services (usually obtained from government data). If a survey has to be undertaken first, then the timeframe
extends significantly, to between 1 to 2 years.
Skills:
Good data handling skills, and experience with analyzing large scale household survey data sets. Experience with related statistical
software packages (SPSS, SAS, STATA)
Supporting software: SPSS, SAS, STATA.
Financial cost:
Costs of developing and using the tool can vary enormously, depending on whether a household survey already exists. If it does, the
analysis can be done for around US$10,000.
Limitations:
Benefit incidence analysis does not take behavior into account, i.e. the likely change in demand from households that would result from
policy changes. For methods which handle this, see Tables on “Ex-post behavioral marginal incidence analysis of public spending and
social programs” and “Social Impact Analysis”.
References and applications:
• For an overview of the technique, see Demery (2003), Chapter 2 of the Toolkit for Evaluating the Poverty and Distributional Impact
of Economic Policies.
• Demery (2000) and van der Walle (1998) on the overall technique.
• Castro-Leal, Dayton and Demery (1997) on a group of African countries.
• Castro-Leal (1996) on South Africa.
• Demery et al. (1995) on Ghana.
• Devarajan and Hossain (1998) on benefit and tax incidence analysis in the Philippines.
• Van der Walle (1992) and Lanjouw et al (2001) on Indonesia.
• Van der Walle (2002c) on incidence of public transfers in Yemen.
56
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Tax Incidence Analysis
What is it?
Tax incidence analysis evaluates the distributional incidence of taxation – its incidence for various household groups (on the basis of
income, geographic location, and other dimensions). The taxes have an effect on real income directly or via prices. (See also Table on
Benefit Incidence Analysis).
What can it be used for?
Tax incidence analysis can be used to analyze the distributional impact of taxes or subsidies. It can also be used to analyze the
distributional impact of other exogenous changes in prices, and publicly provided services.
What does it tell you?
The tool estimates the effect of changes in prices and incomes on the welfare of individuals or households. Most analysis is concerned
with the share of taxes paid by different groups. Taxation is understood as a loss in real income.
Complementary tools:
Tax incidence analysis can be complemented by the analysis of the statutory incidence of taxation (i.e. the analysis of the rules which set
who has to pay which taxes) and by the analysis of the functioning of the tax collection processes (see Tables on Institutional Analysis
and Quantitative Service Delivery Surveys). As tax incidence analysis, benefit incidence analysis (simple and marginal) assesses the
incidence of benefits, and behavioral BIA assesses distributional changes from change in benefits, taking into account reactions to the
change. (See Tables on these two techniques).
Key Elements:
The technique (1) defines the groups of interest, typically in terms of income/consumption, geographic location, gender, ethnicity, age,
socio-economic group, etc. and (2) calculates the taxes paid by each household groups. To do so, one needs to recognize that the
statutory incidence of taxation (those who have to transfer the tax to the government) is not the same as the economic incidence of
taxation (those whose real purchasing power declines because of the tax. The difference results from the fact that different statutory
taxes are shifted among agents. Typically, one assumes that indirect taxes on goods are completely shifted to the consumer (i.e. the
prices reflect the taxes paid by other categories), and that duty taxes are reflected in prices. Hence, taxation has impacts on various
groups of households through the goods, services, transfers and subsidies that they receive. To quantify the tax paid, the technique either
(a) estimates the taxes paid as the official tax rate times the pre-tax value of expenditure (if taxes can be assumed to be collected
according to the letter of the law) or (b) estimates the “effective” tax rate for different categories of expenditure by dividing the tax
base by the actual tax revenues and applies it to these categories.
Requirements
Data/information:
The analysis requires information on tax/subsidy and their changes, and nationally representative household income or expenditure
survey data (e.g. LSMS), including information on specific items to be taxed/subsidized.
Time:
One month, if the data are clean and include a calculated welfare variable (such as household expenditure, consumption or income).
Skills:
Familiarity with the tax system and market structure of the country. Econometric skills and expertise in the supporting software (see
below).
Supporting software: Any statistical software package can calculate point estimates easily (Stata, SPSS, etc). For variances, a matrix programming language
(Gauss, Matlab, SAS IML) is useful. The software package DAD calculates concentration curves and other summary measures of incidence
with standard errors.
Financial cost:
US$15,000
Limitations:
Simple analysis of the incidence of taxes does not account for behavioral changes and hence only provides a first-order approximation of
a tax’s true incidence. Furthermore, inaccuracy can come from the simple assumption of how statutory taxes translate into economic
incidence. In addition, many indirect taxes are also levied on intermediate goods, and estimating the incidence of the tax on final
consumer would require complex models. Finally, the method only focuses on the incidence of taxes and should be complemented by an
analysis of the economic and administrative efficiency of the system.
References and applications:
• For an overview of the technique, see Sahn and Younger (2003), Chapter 1 of the Toolkit for Evaluating the Poverty and
Distributional Impact of Economic Policies.
• Alderman and del Ninno (1999) on the targeting of VAT exemptions in South Africa.
• Ahmad and Stern (1984, 1987, 1990 and 1991) on alternative forms of taxation in India and Pakistan.
• Chen et al. (2001) on Uganda.
• Gibson (1998) on Papua New Guinea and the introduction of VAT.
• Younger et al. (1999) on Madagascar.
• Younger (1993) on Ghana.
57
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Poverty Mapping
What is it?
Technique to estimate geographically disaggregated welfare and inequality levels and changes, for small geographic areas, such as
districts and sub-districts. This allows to take geographic heterogeneity into account.
What can it be used for?
The method can inform the targeting of public resources, and can simulating the geographic impact of policy reforms such as changes in
trade barriers, decentralization, public expenditure, etc. Information disaggregated for small geographic areas is particularly important in
the context of decentralization of public services.
What does it tell you?
The method provides an estimation of poverty/inequality distribution across a country’s sub-regions. It identifies poverty pockets, by
giving satisfactorily precise estimates of poverty at levels of disaggregation far below that allowed by typical household surveys. Poverty
and inequality estimates can then be represented on maps. These maps, on which other variables of interest can be applied, help assess
the spatial impact of policies. Typically, the poverty maps can also include information on education, water, health, public services,
agricultural production, etc. depending on the reform of interest.
Complementary tools:
A poverty map can be merged with other GIS (Geographic Information Systems) containing information on a variety of public actions.
Social Impact Analysis and Participatory Poverty Assessments can help explain the geographic trends revealed in a poverty map.
Key Elements:
The method uses a household survey and a census. It imputes information on poverty and inequality in the census, using estimates from
the household survey, through the construction of consumption-based welfare indicators for small geographic areas. In order to do so, (1)
the variables common to the survey and the census are identified, (2) the survey is used to estimate a prediction model relating
consumption to the variables which the two data sets have in common, (3) the parameter estimates can be applied to the census data
to derive poverty statistics for each household in the census, and (4) poverty and inequality estimates can be calculated for small
geographic areas and transposed into a GIS system.
Requirements
Data/information:
A household survey and a population census, ideally from the same year. If different years are used the compatibility of the two sources
showed be checked by comparing the estimates with basic poverty/inequality statistics in the sample data. In this case, the welfare
estimates refer to the year of the census, whose explanatory variables form the basis of the predicted expenditure distribution
Time:
Depends on the quality of the survey and census data, minimum of two months; six months on average
Skills:
Good knowledge of poverty and inequality measurement. Good data handling skills, and experience with analyzing large scale household
survey and census data sets. Experience with related statistical software packages (SPSS, SAS, STATA)
Supporting software: SPSS, SAS, STATA and GIS software such as ARCView, purpose-written software produced by the World Bank
(http://econ.worldbank.org/programs/poverty/topic/14460/).
Financial cost:
US$20-100,000 depending on level of specialized consultant, availability of counterpart contributions in terms of computational
assistance, etc
Limitations:
Household variables do not capture some unobserved geographic effects (such as climate, quality of local administration etc). Hence, it
may be desirable to complement the analysis using such additional data. Also, when using the technique to simulate the impact of
reforms, behavioral changes are typically ignored.
References and applications:
• For an overview of the technique, see Lanjouw (2003), Chapter 4 of the Toolkit for Evaluating the Poverty and Distributional Impact
of Economic Policies.
• Elbers, Lanjouw and Lanjouw (2002) on the overall approach.
• For purpose-written software and manual, as well as other country applications, see
http://econ.worldbank.org/programs/poverty/topic/14460/.
• Demombynes et al. (2002) on poverty in Ecuador, Madagascar and South Africa.
• Elbers, Lanjouw, Mistiaen, Ozler and Simler (2002) on inequality in Ecuador, Madagascar and Mozambique.
• Elbers, Lanjouw, Lanjouw and Leite (2002) on Brazil.
• Mistiaen (2002) on the analysis of the impact of rice price changes in Madagascar.
• Mistiaen et al. (2002) on health spending in Madagascar.
58
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Ex-post behavioral marginal incidence analysis of public spending and programs
What is it?
This type of analysis combines the analysis of the marginal incidence of benefits with the econometric modeling of household behavior.
The analysis is ex-post, since it focuses on past interventions, drawing lessons for future ones. The methods allow to take the behavior of
both the recipients of public spending or participants in the programs, and of the agents implementing them. Finally, the analysis is
marginal since it focuses on the impact of increases or cuts in public spending and programs.
What can it be used for?
It can be used to explain distributional impacts of public finance or policy reform on individuals and households, allowing for behavioral
responses. This applies to public spending and programs on education, health, and cash transfer programs. It can also be used in the
analysis of other reforms, including land reform, pension reform, and micro-finance programs.
What does it tell you?
The analysis allows to estimate the distributional impacts of changes in public spending or programs, taking the behavioral responses or
beneficiaries and implementing agencies into account. By examining actual change ex-post, these methods can also provide a reality
check for the results of methods that attempt to approximate or predict changes ex-ante.
Complementary tools:
Ex-post Social Impact Analysis can complement these efforts, as can adaptations of tools such as the Quantitative Service Delivery Survey
and Public Expenditure Tracking Surveys that use historical data (see Tables on these tools and techniques).
Key Elements:
The technique entails the econometric analysis of household data on welfare indicators and on receipt of the benefit under consideration
and the modeling of household responses, such as changes in labor supply.
Requirements
Data/information:
Behavioral marginal incidence can be done using: 1) single household survey cross-section with sufficient regional disaggregation and
variance in participation; 2) two or more comparable household cross-sections; 3) Household level panel data, or 4) geographic level
panel data for dynamic marginal incidence
Time:
A few weeks to a few months depending on the quality of the data.
Skills:
Econometric skills.
Supporting software: EXCEL and STATA (or other micro-econometric and spreadsheet software)
Financial cost:
Costs of developing and using the tool can vary, depending on whether household surveys exist already. If they do, the analysis can be
done for around US$10,000
Limitations:
Behavioral benefit incidence analysis typically has more onerous data requirements than simple benefit incidence analysis to allow for
behavioral modeling.
References and applications:
• For an overview of the technique, see van de Walle (2003), Chapter 3 of the Toolkit for Evaluating the Poverty and Distributional
Impact of Economic Policies.
• Lanjouw & Ravallion (1999)
• van de Walle (1994) on Indonesia.
• van de Walle (2002a) on rural roads.
• van de Walle (2002b) on Viet Nam.
• Ravallion (1999)
59
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Ex-ante behavioral marginal evaluation of policy reforms
What is it?
The techniques allow to estimate the situation that would result from changes in policies. The techniques allow for the analysis ex-ante,
i.e. before a reform is implemented, of the distributional impacts of the reform. This analysis is marginal, because it aims at capturing
changes from the existing situation (e.g. new policy, expansion, reduction of existing public actions). The analysis is also behavioral since
the behaviors of various stakeholders are taken into account when defining the counterfactuals.
What can it be used for?
This type of analysis can be applied to types of transfer programs with expected impact on some dimension of household behavior (e.g.
occupational choices, schooling, demand for various goods or services, etc.). This includes, among others, changes in taxes, expenditure,
and targeted programs. It can also be used for any exogenous change in the environment of a household likely to trigger a non-negligible
behavioral response (e.g. accessibility of various types of services, conditions on the labor market, producer and consumer prices).
What does it tell you?
It tells you what would be the likely distributional impacts of policies changes, taking the behaviors of various stakeholders into account.
Complementary tools:
Tools such as Stakeholder Analysis, Social Impact Analysis, and – in some cases – the Social Capital Assessment Tool can help analysts
better understand the variables that are most likely to affect household behavior.
Key Elements:
The technique proceeds as follows: (1) identification of the policy reform to be analyzed; (2) identification of data set and information
sources which contains the necessary information; (3) specification of the economic model which captures the mechanisms likely to
affect the individual or household’s responses to the policy; (4) estimation of the model; (5) and simulation of the policy reform using
the empirical estimate of the model.
Requirements
Data/information:
Household surveys (+ specific surveys or questions depending on the issue of interest)
Time:
6 months with experienced microeconomic modeler
Skills:
Micro-econometric modeling
Supporting software: All software used in micro-econometrics - Stata, SAS, etc.
Financial cost:
Depends on the question being asked and the need for new data. If data is available, the cost can vary from US$10,000 to
US$30,000.
Limitations:
The estimation of behavioral models that fit the policy to be evaluated or designed can be difficult, but can rely on simpler assumptions
(accounting micro-simulation). Second, the approach relies on a structural model, which requires a set of assumptions.
References and applications:
• For an overview of the technique, see Bourguignon and Ferriera (2003), Chapter 6 of the Toolkit for Evaluating the Poverty and
Distributional Impact of Economic Policies.
• Atkinson and Bourguignon (1991) on tax-benefit models.
• Attanasio, Meghir and Santiago (2002) on education choices in Mexico.
• Bourguignon, Ferreira and Leite (2002) on conditional cash transfers in education in Brazil.
• Blundell et al (2000) on tax credit in the U.K.
• Younger (2002) on marginal benefit incidence and education in Peru.
60
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Ex-post impact evaluation methods for assigned programs
What is it?
Methods for assessing the counter-factual to evaluate the poverty impact of assigned programs
What can it be used for?
Any policy, program or shock that are assigned to some observational units but not others, and the units not assigned are largely
unaffected. The units might be people, households, firms, communities, provinces or even countries.
What does it tell you?
It measures the impact, typically defined as difference between the value of the outcome with the program and its value under the
counter-factual (what would have been the value of the indicator in the absence of the program).
Complementary tools:
The best evaluations often combine multiple methods: randomizing some aspects and using econometric methods to deal with the
non-random elements, or by combining matching methods with longitudinal observations to try to eliminate matching errors with
imperfect data.
Complementary tools include Benefit Incidence Analysis, Social Impact Assessment and Demand Analysis, which can help policymakers
track the impact of historical policy changes by combining household survey data with financial or service-provision data .
Key Elements:
The identification strategy establishes the assumptions under which observed outcomes for participants and non-participants can be used
(often in combination with other data) to infer impact. If the program is randomly assigned across the population (every has the same
chance, ex-ante, of being in the program) then the observed ex-post differences in outcomes are attributable to the program. This is not
often the case, however, since programs tend to be purposively targeted to certain groups. In such cases, methods exist to estimate
counterfactuals. Examples include propensity-score matching and “difference-in-difference” methods.
Requirements
Data/information:
Data on relevant outcome indicators for those units who participate versus those who do not. Survey or census data covering participants
and non-participants are essential. The data must include relevant outcome indicators and (depending on the identification strategy) other
relevant covariates for either participation or outcomes.
Time:
Evaluation design should ideally begin even before the policy/program begins; it is often hard to do a good evaluation if one starts late.
“Off-the-shelf” data are sometimes feasible, but it is more often the case that special-purpose data collection is needed and this needs
advance planning.
Skills:
Sufficient knowledge of statistics/econometrics and quantitative data skills. Knowledge of microeconomics often helps. Good knowledge
of the program and its setting is important.
Supporting software: Standard statistical/econometric packages such as STATA are often sufficient. A number of special-purpose STATA routines are available
for evaluation
Financial cost:
Varies enormously, mainly depending on current data availability. The marginal cost of the evaluation can be low in “data rich” settings
and high in “data poor” settings where a lot of primary data collection is called for. Even in data rich settings, supplementary data
collection is often required.
Limitations:
References and applications:
• For an overview of the technique, see Ravallion (2003), Chapter 5 of the Toolkit for Evaluating the Poverty and Distributional
Impact of Economic Policies.
• Galasso et al. (2001) and Angrist et al. (2001) on randomized programs.
• Van de Walle (2002a), Jalan and Ravallion (2003a and 2003b) on propensity-score matching.
• Ravallion et al. (2001) on double-differences techniques.
61
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Demand Analysis: Estimating demand functions
What is it?
Partial equilibrium model that focuses on the level of demand for the commodities an individual, household or producer demands given
the structure of relative prices faced, real income, and a set of individual characteristics. (See also Table on Demand Analysis: Consumer
Assessment)
What can it be used for?
Can be used with a broad range of reforms for which the knowledge of consumer behavior is important. This simple technique, which
focuses on a single good can be particularly useful for the analysis of changes in prices in which the good or service in question has few,
if any, substitutes. This can include changes in tariffs, subsidies, and other prices.
What does it tell you?
How changes in income or in the price of a given good affect the demand of a particular group of consumers or producers.
Complementary tools:
Can be used in conjunction with stakeholder analysis.
The analysis of a complete demand system is often used as the basis for more complex multi-market and computable general equilibrium
models (see Tables on these two techniques). The most common complete demand systems are: Linear Expenditure System (LES); the
Almost Ideal Demand System (AIDS) and the Generalized Almost Ideal Demand System (GAIDS)
Demand analysis is also used to build household models, in combination with supply analysis.
Key Elements:
Methodologically there are two main approaches to estimate the parameters of a demand equation. One consists of specifying estimable
single equation demand functions in a pragmatic fashion without recourse to economic theory, using reduced-form estimation.
Alternatively one may wish to use the theory of demand to derive an estimable structural model which should provide guidance for the
choice of variables to be included, functional forms, and restrictions on the parameters. This model, although usually difficult to estimate
due to its typical high nonlinear nature, provides straightforward interpretations of the transmission channels. When demand analysis is
used for complete models (see for instance Table on multi-market analysis or CGEs), complete systems of demand equations must be
specified and estimated, which are able to take into account the mutual interdependence of large numbers of commodities in the choices
made by consumers.
Requirements
Data/information:
Requires household level consumption and income data, with sufficient variation in prices, either geographically or preferably over time.
Time:
1 to 3 months.
Skills:
For reduced-form models, basic econometric skills may suffice. For structural models, advanced econometric and typically programming
skills.
Supporting software: Software for the analysis of household level data.
Financial cost:
US$5,000 for simple reduced form models; US$35,000 for detailed of especially complex models
Limitations:
Reduced form demand equations are simple to estimate, but the results depend on the choice of functional form and variables included in
the equation. It also requires constancy in elasticities over all values of exogenous variables. Structural models, on the other hand, are
affected by the theoretical underpinnings used to derive the estimable model, and can be extremely complex to estimate.
References and applications:
For the estimation of demand systems:
• Stone (1954) on the Linear Expenditure System,
• Deaton and Muellbauer (1986) on the Almost Ideal Demand System
• Christensen et al. (1975) on the Transcendental Logarithmic System
62
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Supply analysis
What is it?
System of input and output equations used to determine supply responses to changing circumstances by producers (including
households). Supply analysis takes into account changes in both output supply and input/factor demand.
What can it be used for?
Supply analysis can be used to determine the impact of changes in product and factor prices, in technology, and in access on factor
demands (including labor), production, marketed output, aggregate supply, and incomes. For instance, it could be used to estimate the
change in agricultural household production that could result from the liberalization of some markets (inputs, credit, outputs...). More
generally, can be used to analyze the impact on production of the removal of barriers to access or other changes in markets.
What does it tell you?
Supply analysis is central to policy decisions in that it helps us understand the impact that alternative policy packages may have on the
producers themselves. Through the changes it induces in commodity supply and in factor demand, the analysis of production response is
an essential component of models that seek to explain market prices, wages and employment, external trade and government fiscal
revenues.
Complementary tools:
Supply analysis can be combined with demand analysis to build household models.
Institutional analysis and stakeholder analysis can help inform assumptions about constraints to changes in supplier behavior and the
incentive structures within a market. PPA/BA techniques help understand inter-household relationships and how households are likely to
respond.
Key Elements:
In studying supply response, it is important to distinguish between specific goods and broad sector aggregates, and between short-run
and long-run responses. The basic theory of production is based on profit maximization with respect to output and input quantities.
Maximization techniques will yield a set of input demand and output supply functions that are then used to solve for quantities. The
impact of price changes on producers can be estimated for a single commodity, or for a system of commodities in the case where the
firms/households produce multiple outputs. It is also important to distinguish between short-run and long-run outcomes. It is usually
assumed that certain productive factors are fixed in the short run. In agriculture, for instance, the amount of land and the level of
technology do not change within a cropping cycle. Labor, too, may be relatively slow to adjust. For this reason, it can be argued that the
supply elasticity of agriculture is close to zero in the short run. In the medium- and long-term, fixed investments in productive technology
come on-line, and supply can increase.
Requirements
Data/information:
In the case of producing households, this requires household-level production data. For firm-level analysis, firm survey data is needed.
Time:
Between 1 and 3 months if the data is available
Skills:
Advance econometric techniques
Supporting software: Advanced econometric software, such as SAS, STATA, etc
Financial cost:
US$10,000 to US$30,000
Limitations:
Despite its different focus on short-run and long-run effects, supply analysis is a static tool. In addition, at the firm level many decisions
are based on expectations that are difficult to model.
References and applications:
• López et al. (1995) on Mexico.
63
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Household Models
What is it?
Micro-econometric models that integrate producer, consumer and worker decisions into a household problem. These models reflect the
fact that many households, especially in rural areas, are simultaneously units of production and consumption.
What can it be used for?
In the context of farm households, when markets are perfect the only link between production and consumption decisions is through the
level of farm income from production. If there are market imperfections, policies that affect the prices of goods (factors) both produced
(used) and consumed (sold) thus have complex implications for production and welfare. These models have been used to examine a
wide range of policy reforms, such as price and marketing changes, market failures, and taxation. In addition, separate models can be
constructed for different groups to examine structural differences in the impact policies have on these different groups.
What does it tell you?
The models allow to estimate the response of households to changes, in terms of their consumption, production, labor supply, and more
generally any allocation of resources within the household.
Complementary tools:
• When production (labor) exceeds consumption (production needs), the household will be a net supplier of products (labor), and vice
versa. In those circumstance, demand and supply analysis can be a complement to household models.
• Also, if there are no market failures the household behaves as if production and consumption decisions were taken sequentially, in
which case theory of production (i.e. supply analysis) and consumption (i.e. demand analysis) applies.
• Social impact analysis and beneficiary assessment, which looks at household-level determinants of behavior, can provide information
on household preferences and likely switching behavior in the event of a reform.
Key Elements:
If the household model is separable (i.e. production and consumption decisions can be assumed to be taken sequentially), the problem
can be divided into two parts (demand and supply). If the model is not separable, the estimation of production and consumption must be
done simultaneously. One possibility is proceeding with a reduced form approach. A second possibility is the calibration and simulation of
a structural household model.
Requirements
Data/information:
These models require integrated household surveys. Information is needed both on the demand side and the supply side. Ideally, the
models would also account for the allocation of time within the household, which requires data on factors that do not usually appear on
consumption or production surveys, such as allocation of time to child care, or other unremunerated work (e.g. time spent fetching
water).
Time:
If an integrated household survey exists, a few months
Skills:
Advanced experience with household surveys and econometric skills.
Supporting software: Statistical packages for the analysis of household data, including Stata, SPSS, and other software.
Financial cost:
US$10,000 - US$30,0000
Limitations:
References and applications:
• For an extensive review of these models see Sadoulet and de Janvry (1995).
• Singh, Squire and Strauss (1986) on impact of price changes.
• De Janvry et al. (1991) on household models for agricultural households.
64
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Partial Equilibrium Analysis: Multi-Market Models
What is it?
Multi-market models belong to the class of partial equilibrium models. They use partial equilibrium analysis of the impact of changes in
prices and quantities in selected markets on household income and expenditure. They specify a system of demand and supply
relationships for a few sectors of the economy, so that the impact of policies on one sector can be seen on other sectors in the economy.
What can it be used for?
Multi-market models are useful to analyze the poverty and distributional impact of policies that affect the prices and quantities of a small
group of commodities. For example they can be used in estimating distributional impacts of the imposition or change in taxes, subsidies,
quotas, tariffs on specific commodities; rise or fall in the price of imported or exported commodity.
What does it tell you?
Multi-market models allow the estimation of the impact of a policy or external shock on prices and output in a few sectors, and on
household well-being.
Complementary tools:
• Stakeholder analysis can be useful to identify different groups of interest.
• Multi-market models are not general equilibrium models, since they are not necessarily fully specified. If the policy reform is likely to
have general equilibrium impacts, the analysis should be complemented by a CGE model.
Key Elements:
A multi-market model expands the traditional benefit incidence analysis to capture the induced substitution effects across selected goods
in response to policy reform. The first step with this approach would be the identification of the market or markets where the policy
reform under analysis is expected to have a direct effect. Household survey information would then be used to derive estimates of
income, own-price and cross price elasticities of demand for the entire set of interlinked markets. Market closure (either price or quantity
clearing) is imposed for each good in the system of equations. In short, multi-market models involve a system of equations, representing
actors (producers, consumers, government), production or profit functions, factor and product markets, income accruing to the owners of
productive inputs (including labor), and final consumption.
Requirements
Data/information:
Multi-market models require (1) a disaggregated set of data on income or consumption distribution across households, (2) a complete
parameterization for supply and demand functions in the market(s) directly affected by the policy reform, (3) a determination of the
closures of the market(s) being modeled, (4) software to solve a system of potentially non-linear equations for the endogenous prices
and quantities, and (5) a quantitative mapping of these endogenous variables into the income and consumption of households.
Time:
The required time to perform an analysis based on partial equilibrium models depends to a large extent on data availability and degree of
sophistication of the econometric model. It could vary from about one week for a simple model to three months for very detailed models
Skills:
Familiarity with basic partial equilibrium modeling and micro-econometric estimation techniques
Supporting software: Stata, SAS, GAMS
Financial cost:
US$5,000 for simple models; US$25,000 for detailed or especially complex models
Limitations:
These models are limited to selected markets, and hence ignore other interlinked markets by design.
It is also prudent for the analysis to conduct sensitivity analysis of the results for different values of the parameters used in the model.
References and applications:
• For an overview of the technique, see Arulpragasam and Conway (2003), Chapter 12 of the Toolkit for Evaluating the Poverty and
Distributional Impact of Economic Policies.
• Binswanger and Quizon (1984, 1986) on agriculture in India.
• Dorosh, del Ninno and Sahn (1995) on food aid in Mozambique.
• Minot and Goletti (1998) on rice refom in Vietnam.
65
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Partial Equilibrium Analysis: reduced-form estimation
What is it?
Partial equilibrium model focusing on the effects of policy changes (including interest rate changes, taxes, etc.) or exogenous shocks (a
change in import tariffs in another country, or a terms of trade shock) on a variable of interest, such as aggregate consumption or
income.
What can it be used for?
Analysis of partial equilibrium on the basis of reduced-form estimation is one of the most common applications of econometric analysis,
and can be used to examine a myriad of different outcomes. It can be applied to most policy changes or exogenous shocks. It is most
useful for PSIA of policy reforms which have significant indirect effects. For example, simple tax incidence analysis (see Table on this
method) can analyze the direct distributional impacts of tax changes, but does not capture the impact of tax changes on the overall
economy and growth, thereby only providing a partial answer to the question of impact. Partial equilibrium analysis with reduced-form
estimation can capture this indirect impact and provide a first approximation of the expected impact on aggregate incomes.
What does it tell you?
It can provide a good estimation of the impact that changes in a given policy or exogenous shocks have on a particular variable of
interest. Once a model containing the policy variable and the variable of interest has been estimated, reduced-form models can be used
to simulate the impact of policy alternatives.
Complementary tools:
Reduced-form estimation can be useful to understand the macroeconomic impact of a policy intervention on a selected variable of
interest. There is often a need to complement the analysis by the use of household surveys to map these impacts into distributional
changes. Stakeholder analysis can be useful to identify different groups of interest for the analysis.
Key Elements:
Reduced-form estimation assumes an underlying system of demand and supply equations but the model itself does not fully specify the
whole array of economic and social interactions. Rather, the model is solved to derive a single estimating equation: an econometric model
that relates the outcome and the policy variables or shock of interest. This can be done on the basis of two observations separated over
time by a policy change. When using a single cross-sectional dataset, there must be significant variation across the sample population to
estimate the equation. Analysis on aggregate units, such as cross-country regressions, should ideally be conducted on panels of
cross-sectional and time-series data.
Requirements
Data/information:
Reduced-form models require information on the variable of interest, and its hypothesized determinants. The specific data requirements
depend on the unit of analysis, from household or individual level to country level.
Time:
The required time to perform analysis based on partial equilibrium model and reduced-form estimation depends to a large extent on the
data availability and the degree of sophistication of the econometric model. It could vary from a week for a simple model to three
months for very detailed models.
Skills:
Econometric skills
Supporting software: Eviews, STATA, Gauss, etc.
Financial cost:
US$5,000 for simple models, US$25,000 for detailed, complex models.
Limitations:
The elasticities of the policy variable to the variable of interest are often based on cross-country regressions, and may differ from the
elasticity in the country itself.
References and applications:
• Barro (1997)
• Quah and Durlauf (1999)
• Dollar and Kraay (2002)
66
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Social Accounting Matrices
What is it?
A social accounting matrix (SAM) is a technique related to national income accounting, providing a conceptual basis for examining growth
and distributional issues within a single analytical framework. It can be seen as a tool for the organization of information in a single
matrix of the interaction between production, income, consumption and capital accumulation.
What can it be used for?
SAMs can be used for some simple policy simulations
What does it tell you?
SAMs can be applied to the analysis of the interrelationships between structural features of an economy and the distribution of income
and expenditure among household groups.
Complementary tools:
SAMS would complement and be complemented by the use of household surveys to map impacts into distributional changes. Stakeholder
analysis can be useful to identify different groups of interest.
Key Elements:
A typical SAM contains entries for productive activities, commodities, factors, institutions, the capital account, and the “rest of the world.”
An activity produces (and receives income from) commodities, buys commodities as production inputs, pays wages to labor, rents to
capital, and taxes to the government. Factor income accrues to households as owners of the factors. The SAM can be constructed to
distinguish household groups by, for example, sources of income. SAM techniques select some accounts as exogenous, and leave the
remaining accounts endogenous. In part, this selection can be made with a sound theoretical basis, but it is often arbitrary. For example,
if the SAM contains an account for agricultural production and one for transportation, an experiment can be run by imposing some
exogenous change (a “shock”) to agriculture while leaving the transport sector fixed, or while allowing the transport sector to adjust
endogenously as a result of the shock
Requirements
Data/information:
The data sources for a SAM come from input-output tables, national income statistics, and a household survey with a labor module.
Time:
About three months for a moderately detailed SAM.
Skills:
Working with household datasets; strong knowledge of national accounts; use of Excel and maybe GAMS (for using dedicated software)
Supporting software: Excel and GAMS-based dedicated software; STATA, SAS or SPSS for working with household datasets
Financial cost:
US$25,000 when the data is available. This does not include the cost of developing a new household survey.
Limitations:
SAM models have at least two major drawbacks. First, prices are fixed, and do not adjust to reflect changes in, say, real activity. As a
result, supply is either perfectly elastic (if chosen to be endogenous) and entirely demand driven, or perfectly inelastic – that is, supply is
constant. Second, the results of the simulations vary greatly depending on the assumptions made about which accounts are exogenous
and which endogenous.
References and applications:
• For an overview of the technique, see Round (2003), Chapter 14 of the Toolkit for Evaluating the Poverty and Distributional Impact
of Economic Policies.
• Pyatt and Round (1985).
• Powell and Round (2000).
• Reinert and Roland-Holst (1997).
• Sadoulet and de Janry (1995).
• Tarp, Roland-Holst and Rand (2002).
67
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
CGE models
What is it?
CGE models are completely-specified models of an economy, or a region, including all production activities, factors and institutions. The
models therefore include the modeling of all markets (in which agents’ decisions are price responsive and markets reconcile supply and
demand decisions) and macroeconomic components, such as investment and savings, balance of payments, and government budget.
What can it be used for?
CGEs can be used to analyze the poverty and social impacts of a wide range of policies, including exogenous shocks (exchange rate,
international prices, etc.), changes in taxation, subsidies and public expenditure (including changes in trade policies), and changes in the
domestic economic and social structure (including technological changes, asset redistribution, human capital formation).
What does it tell you?
CGE models are best chosen for policy analysis when the socioeconomic structure, prices, and macroeconomic phenomena all prove
important for the analysis. CGEs allow to take into account all the sectors of the economy as well as the macro-economy, and hence
permit the explicit examination of both direct and indirect consequences of policies. This is particularly important for those policy reforms
that are likely to play a large role in the economy and might have important impacts on other sectors and/or on the flow of foreign
exchange or capital.
Complementary tools:
Other tools described here belong to this class of models, with an additional model to take distribution into account: the 1-2-3 PRSP,
IMMPA and the Augmented CGE Model with Representative Household Approach. See their respective Tables.
Key Elements:
A CGE can be described by specifying the agents and their behavior, the rules that bring the different markets in equilibrium, and the
macroeconomic characteristics. CGEs are based on SAMs (see Table on Social Accounting Matrices), and can be distinguished by the
complexity and level of disaggregation of productive activities, factors and institutions, including households.
Requirements
Data/information:
CGE models are data-intensive. They are constructed from combined national accounts and survey data. These are first compiled into a
SAM, which is then used as the foundation of the CGE.
Time:
A few months to a year, depending on the existence of a SAM, or of another CGE model built to address a different question. Even these
simple CGEs can be complex and time consuming. An alternative is to use a previously constructed CGE. For example, Ianchovichina et
al. (2001) use a CGE model constructed by the Global Trade and Analysis Project (GTAP) to examine the impact of NAFTA on household
welfare in Mexico. However, the use of a previously constructed simple CGE can limit the number of policy changes that can be simulated
(in the previous example, the model was constructed to examine trade policy, and did not contained domestic taxes or public
expenditure).
Skills:
Experienced modelers with substantial prior exposure to Computable General Equilibrium models are required
Supporting software: Excel, Eviews, Gauss
Financial cost:
US$25-75,000 depending on existing data
Limitations:
The results of CGE simulations depend at least partly on the assumptions made in the model, such as the “closure” rules. These ensure
that macroeconomic accounts (fiscal, trade, savings-investment) balance. Whether they are fixed exogenously or allowed to balance
endogenously, and how they balance, can have a significant impact on the outcomes. In addition, the production accounts specified in
most available CGEs are too aggregated to identify the impact of policy changes in one component of one account. Many CGEs have at
most two agricultural activities, one each for tradable and non-tradable crops, or food crops and cash crops.
References and applications:
• Dervis et al. (1982) and Shoven and Whalley (1992) for summaries of CGE models use.
• Ianchovichina, Nicita and Soloaga (2001). GTAP models at http://www.gtap.agecon.purdue.edu.
68
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
PovStat
What is it?
An Excel based software program which simulates the changes in poverty and inequality over time resulting from changes in output and
employment growth.
What can it be used for?
PovStat may be used to simulate the poverty and inequality impact of policies affecting sector-level output and employment growth rates.
What does it tell you?
PovStat simulates poverty and inequality measures under alternative growth scenarios. Forecasts of varying levels of complexity may be
computed depending on the availability of reliable data and the extent to which factors influencing poverty levels are incorporated. The
simulations vary according to optional projection parameters.
Complementary tools:
Other software programs that provide poverty and inequality forecasts include SimSIP Poverty (see Table on SimSIP), and DAD (a
software for distributive analysis).
Social impact analysis and institutional analysis could complement this analysis by identifying constraints to market participation by
certain groups which can affect poverty and inequality estimates.
Key Elements:
On the basis of household-level data, the software translates differential output and employment growth across sectors into differential
growth in per capita income or consumption of households across those sectors. The tool simulates the impact of policies affecting output
on poverty using the fact that poverty changes can be decomposed into two parts: a component related to the uniform growth of income,
and a component due to changes in relative income. The simulations are made under the assumption either that the policy analyzed will
be distribution neutral, or conversely assuming a specific quantifiable form for the distributional change. Changes in occupational
distribution are accommodated through reweighing of sample households.
Requirements
Data/information:
This program requires unit record household survey data. Also, a poverty line, survey year, and forecast horizon are parameters that must
be provided by the user. Macroeconomic variables at the nationally aggregated or sectorally disaggregated level and growth rates of
income, employment and population are also required. In addition, the user can input change in CPI and GDP deflator, change in relative
price of food and shares of food in CPI, and changes in poverty line consumption bundle. This allows to generate different types of fore
casts optional projection parameters such as employment shifts across sectors. The software can also be adapted for grouped data.
Time:
1-2 days to format the household survey data, collate and check exogenous economic variables and enter everything into PovStat.
Skills:
Familiarity with Excel and appropriate household data handling software (such as Stata). Also, with PovCal if synthetic data from a
grouped distribution are to be used
Supporting software: Excel
Financial cost:
Limitations:
PovStat does not capture second round effects. These may be captured by CGE models.
References and applications:
• For an overview of the technique, see Datt, Ramadas, van der Mensrugghe, Walker and Wodon (2003), Chapter 10 of the Toolkit
for Evaluating the Poverty and Distributional Impact of Economic Policies.
• Datt and Walker (2002).
• Software available at http://www.worldbank.org/psia, section on Tools and Methods.
69
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
SimSIP Poverty
What is it?
SimSIP Poverty is a generic Excel based simulator, which allows to estimate the changes in poverty and inequality over time resulting
from changes in output and employment growth.
What can it be used for?
This tool may be used to simulate the poverty and inequality impact of policies affecting sector-level output and employment growth.
What does it tell you?
It simulates poverty and inequality measures nationally and within sectors (urban and rural; agriculture, manufacturing and services). It
may simulate the impact of various sectoral patterns of growth and population shifts between sectors on future poverty and inequality.
Complementary tools:
Other tools for poverty forecasts include PovStat (see Table on PovStat), and DAD (a software for distributive analysis)
Social impact analysis and institutional analysis could complement this analysis by identifying constraints to market participation by
certain groups which can affect poverty and inequality estimates.
Key Elements:
On the basis of existing information on group level household survey data (typically by deciles or quintiles), the software translates
differential output and employment growth across sectors into differential growth in per capita income or consumption of households
across those sectors. The tool simulates the impact of policies affecting output on poverty using the fact that poverty changes can be
decomposed into two parts: a component related to the uniform growth of income, and a component due to changes in relative income.
The simulations are made under the assumption either that the policy analyzed will be distribution neutral, or conversely assuming a
specific quantifiable form for the distributional change. Changes in occupational distribution are accommodated through reweighing of
sample households.
Requirements
Data/information:
SimSIP Poverty uses grouped household data, typically groups by income: the mean income or consumption by group and the share of
these groups are required. In addition, SimSIP Poverty requires macroeconomic data at a nationally aggregated or sectorally
disaggregated level. This includes for example past or expected growth rates of output, employment and population by sector. Finally, the
population size and growth, and a poverty line are necessary for calculating poverty incidence.
Time:
1 day to gather the data on population shares and mean income/consumption by group, check the realism of scenarios, and enter the
data into the software.
Skills:
Familiarity with Excel
Supporting software: Excel
Financial cost:
Limitations:
SimSIP does not capture second round effects. These may be captured by CGE models.
References and applications:
• For an overview of the technique, see Datt, Ramadas, van der Mensrugghe, Walker and Wodon (2003), Chapter 10 of the Toolkit
for Evaluating the Poverty and Distributional Impact of Economic Policies.
• Wodon et al. (2003).
• Ramadas et al. (2002).
• Software available at www.worldbank.org/simsip.
70
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
123 PRSP
What is it?
123PRSP (one country, two sector, and three goods) is a static computable general equilibrium (CGE) model.
What can it be used for?
123PRSP can be used to analyze the impact of macroeconomic policy and external shocks on income distribution, employment and
poverty.
What does it tell you?
It allows for a forecast of welfare measures and poverty outcomes consistent with a set of macroeconomic policies in the context of a
very simple general equilibrium model. For a given set of macroeconomic policies, 123PRSP generates a set of wages, sector specific
profits and relative prices that are mutually consistent. The projected changes in prices, wages and profits are then inputted into
household data on wages, profits and commodity demand for representative groups, or segments of the distribution. In principle,
123PRSP can calculate the policy impact on each household in the sample so as to capture the effect on the entire distribution of
income. For a given poverty line, 123PRSP can also compute the effect of different poverty measures.
Complementary tools:
Analysis of impacts on income distribution could be complemented by social impact analysis and institutional analysis, which look at
variables that would affect household participation in growth.
Scenario analysis, which helps policymakers assess the effects of major discontinuities on economic projections, could complement CGE
models operating on a long time horizon.
Key Elements:
123PRSP can be viewed as a middle ground between consistency models (such as RMSM-X), and more sophisticated approaches such us
disaggregated computable general equilibrium models. The former are simple to estimate and use, but take the two most important
determinants of poverty -economic growth and relative prices- as exogenous. The latter\ are useful to capture the poverty impacts of
policies but are too data-intensive and difficult to master. One salient feature of 123PRSP is its modular approach; by linking several
existing models together it can make use of individual modules which already exist. Further, if for data or other reasons a particular
module is not available the rest of the framework can be implemented without it.
Requirements
Data/information:
The 123PRSP model requires national accounts, a social accounting matrix (SAM), and some basic distributional data or a household
survey. The model builds on an existing static aggregate model, such as the IMF’s Financial Programming Model (containing a consistent
set of national accounts which are linked with fiscal balance of payments and monetary accounts). Macroeconomic policies are then fed
into the “Get Real Module” or an alternative country specific model of long-run growth determination and into a trivariate VAR module of
short run fluctuations. This trivariate module would require historical national account data. Both long-run and short-run projections would
then feed into the 123 model to generate projections on changes in wages, profits and the prices of the three goods, which in turn are
fed in a household data module to capture the effects of macroeconomic policies on poverty.
Time:
About three months if a household survey and the macro model are available
Skills:
Experienced modelers with expertise in financial programming and advanced time series econometrics.
Supporting software: Eviews, Excel
Financial cost:
Without the cost of developing the macro model or the Household survey, about US$25,000 to set a new model.
Limitations:
As noted above, 123PRSP adopts several strategic simplifications in order to make the model user friendly. The cost of adopting this
approach is that the causal chain from macroeconomic policies to poverty is in one direction only. The model in this regard does not
capture the feedback effect of changes in the composition of demand (due to shifts in the distribution of income) on macroeconomic
balances.
References and applications:
• For an overview of the technique, see Devarajan and Go (2003), Chapter 13 of the Toolkit for Evaluating the Poverty and
Distributional Impact of Economic Policies.
.
71
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Poverty Analysis Macroeconomic Simulator (PAMS)
What is it?
PAMS is an econometric model that links a macro-consistency model or macroeconomic framework to a labor-poverty module.
What can it be used for?
PAMS can be used to address the impact of macroeconomic policies and exogenous shocks (such as an exogenous rise or fall in output
growth, or a change in the sectoral composition of output) on individual households.
What does it tell you?
PAMS can produce historical or counterfactual simulations of: (i) alternative growth scenarios with different assumptions for inflation,
fiscal, and current account balances. These simulations allow for testing tradeoffs within a macro stabilization program. (ii) Different
combinations of sectoral growth (agricultural or industrial, tradable or nontradable goods sectors) within a given aggregate GDP growth
rate (iii) tax and budgetary transfer policies
Complementary tools:
Stakeholder analysis can be useful to identify groups to inform the selection process of micro categories. Social impact analysis and
institutional analysis could help analysts identify constraints to market participation by certain groups which would affect poverty and
inequality estimates.
Key Elements:
PAMS has three main components: (i) a standard aggregate macro-framework that can be taken from any macro-consistency model (for
example, RMSM-X, 123) to project GDP, national accounts, the national budget, the balance of payments, price levels, etc. in aggregate
consistent accounts; (ii) a labor market model breaking down labor categories by skill level and economic sectors whose production total
is consistent with that of the macro framework. Individuals from the household surveys are grouped in representative groups of
households defined by the labor category of the head of the household. For each labor category, labor demand depends on sectoral
output and real wages. Wage income levels by economic sector and labor category can thus be determined. In addition, different income
tax rates and different levels of budgetary transfers across labor categories can be added to wage income; and (iii) a model that uses the
labor model results for each labor category to simulate the income growth for each individual inside its own group, assumed to be
the average of its group. After projecting individual incomes, PAMS calculates the incidence of poverty and the inter-group inequality
Requirements
Data/information:
The model requires national accounts (with a breakdown by sector) and household survey data with income/expenditure data by unit,
and a wage and employment breakdown by sectors
Time:
With a macro model the time needed to build a PAMS would be about three months:
(i) One month to select/extract categories of households from the household survey and match the economic sectors from the macro
model.
(ii) One month to link the macro model to the household survey data, and
(iii) One month to run the macro and household module together and adjust.
Skills:
Knowledge of (i) National Accounts based macroeconomic models, (ii) of basic labor demand models and (iii) of the structure of
household surveys is required
Supporting software: Eviews, Excel
Financial cost:
US$25,000 when the data is available. This does not include the cost of developing a macro model or a new household survey
Limitations:
The main limitation is the lack of feedback of the micro model into the macro model.
References and applications:
• For an overview, see Pereira da Silva, Essama-Nssah and Samake (2003), Chapter 11 of the Toolkit for Evaluating the Poverty and
Distributional Impact of Economic Policies.
• Pereira da Silva, Essama-Nssah and Samake (2002).
72
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Integrated macroeconomic model for poverty analysis (IMMPA)
What is it?
IMMPA is a dynamic computable general equilibrium (CGE) model.
What can it be used for?
IMMPA can be used to analyze the impact of macroeconomic policy and external shocks on income distribution, employment, and
poverty.
What does it tell you?
One of the main features of IMMPA is that it integrates the real and financial side of the economy; in this regard, IMMPA is useful to
analyze both the impact of structural reforms (such as changes in tariffs or the composition of public expenditure) and the effects of short
term stabilization policies (such as a cut in domestic credit or a rise in deposit interest rates). The detailed treatment of the labor market
is key for the assessment of the poverty reduction impact of macroeconomic policies. Also it is useful to make the distinction between
rural and urban sectors by completing separate projections on output and employment fluctuations for both areas, and therefore to study
poverty in different geographical areas
Complementary tools:
IMMPA would complement and be complemented by the use of household surveys to map impacts into distributional changes.
Stakeholder analysis can be useful to identify different groups of interest. Social impact analysis and institutional analysis could help
analysts identify constraints to market participation by certain groups which would affect poverty and inequality estimates.
Key Elements:
The main distinguishing features of IMMPA from other CGE models are the following. First, IMMPA has a very detailed specification of the
labor market, which is the main transmission channel of macroeconomic shocks and adjustment policies to economic activity, employment
and relative prices. The labor market specification allows for a disaggregation at the urban and rural levels and in turn, for each of these
areas, in the formal and informal sectors. Second, IMMPA links real and financial sectors through an explicit treatment of the financial
system. Third, the model emphasizes the negative effect of external debt on private investment and therefore incorporates the possibility
of debt overhang. Finally, IMMPA accounts explicitly for the channels through which various types of public investment outlays affect the
economy.
Requirements
Data/information:
The greatest drawback of any fully specified CGE model is the time and data required to construct it. The model must be constructed from
combined national accounts and survey data. These are first compiled into a SAM, which is then used as the foundation of the model.
IMMPA for example consists of 131 equations, more than 30 exogenous variables and more than 200 endogenous variables.
Time:
The process can take more than a year, and rarely less than a few months.
Skills:
Experienced modelers with substantial prior exposure to Computable General Equilibrium Models are required
Supporting software: Eviews, Excel
Financial cost:
US$75,000 to develop the IMMPA general equilibrium model
Limitations:
CGE simulations depend to a large extent on the assumptions made in the model, especially those that are required to close the model.
They are also data-intensive and difficult to master, something that could limit its usefulness under tight deadlines or capacity constraints.
References and applications:
• Agenor, Izquierdo, Fofack (2003).
.
73
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Augmented CGE Model with Representative Household Approach
What is it?
This technique is based on a computable general equilibrium model with representative households that are linked to a household
module.
What can it be used for?
Representative Household Models can be used to analyze the impact of macroeconomic policy and external shocks on income
distribution, employment, and poverty
What does it tell you?
Representative household models allow for a forecast of welfare measures and poverty outcomes consistent with a set of macroeconomic
policies in the context of a general equilibrium model
Complementary tools:
Key Elements:
Requirements
The key features of the Representative Household Approach are (i) a Computable General Equilibrium (CGE) model that incorporates
markets for factors and commodities and their links to the rest of the economy, which generates equilibrium values for employment,
wages and commodity prices as well as its “extended” functional distribution (i.e. labor differentiated by skill, education, gender, region,
and sector of employment); and (ii) a mapping from the extended functional distribution into the “size” distribution (the distribution of
income across different households). In this approach, the Representative Households that appear in the CGE (corresponding to
aggregates or averages of groups of households) play a crucial role: the “size” distribution is generated by feeding data on the simulated
outcomes for the Representative Household into a separate module that contains additional information about each household.
Data/information:
Representative Household Models require a social accounting matrix (SAM) and distributional data describing the Representative
Household groups or, more specifically, a household survey
Time:
Only a few days to generate a base solution if data and skills are available. Between six months and a year to collect data and work
with the simulations
Skills:
Experienced modelers with substantial prior exposure to Computable General Equilibrium models are required.
Supporting software: Excel, Eviews, Gauss
Financial cost:
US$25-75,000 depending on the data that exists
Limitations:
In the absence of a CGE model to feed in the Representative Household module, it is data-intensive and difficult to master
References and applications:
• For an overview, see Logfren, Robinson and El-Said (2003), Chapter 15 of the Toolkit for Evaluating the Poverty and Distributional
Impact of Economic Policies.
• Robillard, Bourguignon and Robinson (2001) on Indonesia.
• Coady and Harris (2001) on Mexico.
• Lofgren et al. (2002).
74
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Social Risk Assessment
What is it?
Analytical approach that uses qualitative methods to identify and assess risks to, and from the policy reform, and to inform risk
management strategies
What can it be used for?
Risk assessment is relevant to all reforms that have significant poverty and social impacts. Particularly useful for decentralization reforms;
civil service retrenchment; land reform; financial sector reform (e.g. regulatory reform, privatization of SOE); labor market reform (e.g.
minimum wage legislation); social safety nets; pension reforms; and agricultural reform (e.g. changing domestic subsidies and taxes,
eliminating marketing boards). Social Risk Assessment follows an analysis of stakeholders, institutions, and social impacts, and feeds into
M&E systems by establishing a baseline of objective risk indicators for country- and sector-level risk assessments
What does it tell you?
Helps to (a) identify risks that could undermine policy reform objectives; (b) inform analysis of alternatives in policy design, and inform
design of complementary measures when it appears that a reform will have adverse impacts; (c) develop risk management strategies for
the identified risks to, and from the policy reform. Risk assessment involves the following steps: (i) identification of assumptions –
implicit or explicit – about what should (or should not) happen in order to for a policy to achieve its goals; (ii) assessing the likelihood
that these assumptions will hold, and their importance to policy; (iii) assessing the impact of policy change should the assumptions prove
invalid; (iv) informing risk management strategies to address important risks that are unavoidable.
Complementary tools:
Normally conducted after Stakeholder Analysis and Institutional Analysis, as a complement to impact analysis. Provides crucial insights for
Scenario Analysis, and M&E systems
Key Elements:
(1) Identification of risks, (2) Assessment of the likelihood of occurrence and importance of each risk to the reform, and (3) Elaboration
of adequate risk management strategies. Risks are identified from assumptions about transmission channels and likely impacts. This
should cover country risks (e.g. conflict and violence, political instability, ethnic or religious tension); institutional risks (e.g.: weak
governance or capacity, design complexity); political economy risks (e.g. capture of benefits, opposition or distortion by influential
stakeholders); and exogenous risks (e.g. terms of trade, climate effects). Information about risks is gathered from (i) secondary
literature, (ii) discussions with Bank staff and other partners; (iii) existing agencies that assess country risks; and (iv) questionnaires,
in-depth interviews or focus groups with key informants from government agencies, non-government organizations and firms. This
information is validated through triangulation and crosschecking among information obtained from these different sources.
Requirements
Data/information:
Secondary material, including objective risk indicators, and risk assessments available from country databases, international risk rating
agencies (e.g. EIU risk rating, ICRG, TI), and social science research, as well as from implementing agencies and partners. Primary data,
that identifies the spectrum of risks to, and from the reform, illustrates their likelihood of occurrence and importance to the policy, and
helps develop adequate risks management strategies.
Time:
Can be undertaken rapidly (2-4 person weeks) in country, depending on reform complexity.
Skills:
Sociological and anthropological training are helpful. It is crucial to have an in-depth knowledge of the country-context, reform area,
country- and sector-level assessment of key assumptions regarding the reform, and objective country-level risk indicators.
Supporting software: N/A
Financial cost:
Can be undertaken at relatively low-cost (US$16-25,000)
Limitations:
If poorly facilitated or done with contentious stakeholders, assessment can easily produce skewed perceptions of risks that are based, for
instance, on dogma or political calculation, rather than reflection and deliberation. As findings are necessarily based on stakeholder
understanding of complex issues, it is key to validate results through reiteration exercises.
References and applications:
•
•
•
•
•
•
Beck et al (2002).
Kaufman and Kray (2000).
World Bank (2002c).
Economist Intelligence Unit Country Risks Ratings (http://www.eiu.com)
Transparency International Corruption Perception Index (http://www.transparency.org)
International Country Risk Guide ratings (http://www.prsgroup.com)
75
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Scenario Analysis
What is it?
Scenario analysis is a participatory exercise based on a facilitated process of brainstorming, rigorous data gathering to explore the issues
raised in brainstorming and the creation of three to four plausible future situations (scenarios) in which a reform will play out. These
scenarios are differentiated by plausible discontinuities (such as a change in government, a currency devaluation or a major shift in
commodity or input prices), but take into account significant predictable factors (such as demographic trends).
What can it be used for?
Scenario analysis is forward-looking and is generally used to analyze “lumpy” investments or major changes in strategic direction. The
process is particularly adapted to bringing the perspectives of different stakeholders together around contentious decisions. “Civic
scenarios” have been used to bring leaders from different political groups together to lie out alternative paths during government
transition in South Africa and the transition away from violence in Colombia and Guatemala. Scenarios have also been used to bring
community leaders, environmentalists, politicians and transport specialists together to make long-term strategic plans for state-level
transport investment in the United States.
What does it tell you?
Scenario analysis lets policy-makers: (i) “pre-test” the performance of a policy reform in different plausible situations, allowing for the
creation of alternate plans; (ii) assess the level of ownership for a reform agenda among key stakeholders; (iii) get support for a reform
agenda by including relevant stakeholders in discussions around scenarios to build a shared understanding of key issues in a reform.
Complementary tools:
Normally used in conjunction with economic models, which can serve as inputs to the scenario-building process, and stakeholder analysis,
which helps determine key groups to consider in different scenarios.
Key Elements:
The elements of a complete scenario analysis are: (i) preliminary scenario workshop which brings together relevant stakeholders to
brainstorm the key issues around a reform agenda; (ii) data collection wherein a researcher assembles relevant information around the
issues identified in a workshop; (iii) scenario-building workshop where relevant stakeholders build alternate scenarios; (iv) dissemination
process where scenarios are shortened to one-page briefing notes and shared with the public via newspapers, television and radio
Requirements
Data/information:
Scenario analysis requires: (i) economic information, including standard economic projections; (ii) demographic information; (iii) sectorspecific information relevant to the issues at hand; (iv) a basic profile of a country’s political economy and of ethnic, linguistic and
religious divisions within a country.
Time:
When used to challenge analytic assumptions rather than to build support among stakeholders, the scenario exercise itself could be
completed in three to four staff weeks. A participatory scenario exercise is usually carried out in two to three workshops lasting several
days each. These workshops are usually spread over several calendar months in order to allow time for data collection and to
accommodate the schedules of participants.
Skills:
An individual with strong facilitation skills and specific experience running scenario exercises. Research skills, including familiarity with
economic and demographic trends.
Supporting software: N/A
Financial cost:
A small exercise intended to ensure that the assumptions of policymakers are challenged would cost approximately US$10,000. A full
exercise with participatory workshops designed to build support among stakeholders could cost as much as US$30,000.
Limitations:
Successful scenario analysis is based on the skill of facilitators and the choice of participants. Because the process is participatory and
based on subjective understanding, it is best for strategic rather than tactical questions.
References and applications:
•
•
•
•
Maack (2001).
Pruitt (2000).
Civic Scenarios: Kahane (1996) on South Africa, Kahane (1998) on Colombia.
Planning Scenarios: see experience of Utah at http://www.envisionutah.org/
76
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Public Expenditure Tracking Survey (PETS)
What is it?
A technique to survey service-provides to assess the efficiency of public spending and the quality and quantity of services.
What can it be used for?
PETS can be used for the analysis of public expenditure management reforms, reforms to improve the efficiency of public expenditure,
cross-cutting public sector reforms, anti-corruption, and service delivery reforms.
What does it tell you?
A PETS tracks the flow of resources through the various layers of government bureaucracy, down to the service facilities in order to
determine how much of the originally allocated resources reach each level, and how long they take to get there. It can help identify the
location and extent of impediments to resource flows (financial, staff, equipment). It can therefore evaluates the mechanisms and
incentives responsible for public expenditure leakages, capture and deployment impediments. A PETS focuses on service provider
behavior, incentives, and relationship between providers, policy-makers and users.
Complementary tools:
• A PETS can be cross-validated by a Quantitative Service Delivery Survey (QSDS) which assesses the efficiency of public spending at
the level of service provider.
• A PETS analysis can be linked upstream to public administration surveys, and downstream to household surveys.
• Linking a PETS with household surveys would allow to include the demand for services or outcomes.
• Benefit incidence analysis can be enhanced by using filter coefficients obtained from PETS/QSDS to deflate cost per user to take into
account leakage or capture of funds.
• Institutional and stakeholder analysis can help define the parameters of PETS surveys.
• Citizen Report Cards can serve as a monitoring tool to verify the perceived effectiveness of public services for stakeholders.
Key Elements:
A PETS is typically implemented with the following steps: (1) Consultations with key stakeholders, including government agencies, donors
and civil society organizations are carried out to: define the objectives of the survey, identify the key issues, determine the structure of
resource flows and the institutional setup, review data availability, outline hypotheses and chose the appropriate survey tool. (2) Survey
instruments are then constructed and implemented. The PETS deals with the fact that agents may have strong incentives to misreport
data by using a multi-angular data collection strategy and carefully considering which sources and respondents have incentives to
misreport, and identifying sources that tare the least contaminated by these incentives.
Requirements
Data/information:
In addition to the PETS itself, uses public accounts sample data, preferably panel data, on government spending and information on
outputs of service providers at ministerial, regional, local and service provider levels. Field testing of the survey is key to ensuring high
quality results
Time:
Consultations, design, and pre-testing take several months. The survey itself takes 1-2 months, depending on sample size and data
accessibility.
Skills:
Some prior experience of micro survey work and STATA required, and a detailed knowledge of the relevant institutional context.
Microeconomics of provider behavior (incentives and organization theory).
Supporting software: STATA
Financial cost:
US$60-100,000 plus design
Limitations:
Results suffer from data limitations, i.e. where service provision is not well recorded, or is in-kind. Respondents may have incentives to
misreport information
References and applications:
• For an overview, see Dehn, Reinikka and Svensson (2003), Chapter 9 of the Toolkit for Evaluating the Poverty and Distributional
Impact of Economic Policies.
• Reinikka and Svensson (2002a) for an overview of the approach.
• Reinikka (2001), Reinikka and Svensson (2003), Republic of Uganda (2000 and 2001) on Uganda.
• Government of Tanzania (1999 and 2001) on education and health care in Tanzania.
• Xiao and Canagarajah (2002) on Ghana.
• Das et al. (2002) on Zambia.
• World Bank (2001b) on Honduras.
• See www.publicspending.org and http://econ.worldbank.org/programs/public_services/topic/tools/ for some of the tools
available and their applications.
77
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Quantitative Service Delivery Survey (QSDS)
What is it?
A technique to survey the efficiency of service provision
What can it be used for?
A QSDS can be used for the analysis of public expenditure management reforms, service-delivery reforms, reforms to improve the
efficiency of public expenditure, as well as cross-cutting public sector reforms.
What does it tell you?
A QSDS examines the efficiency of public spending and incentives, and various dimensions of services delivery in provider organizations,
especially at the level of the service facility. It quantifies the factors affecting quality of service such as incentives, accountability
mechanisms, and the relationship between agents and principals.
Complementary tools:
• A QSDS can be cross-validated by a Public Expenditure Tracking Survey (PETS) which tracks the flow of resources from the central
level to the level of service provider. A QSDS analysis can also be linked upstream to public administration surveys, and downstream
to household surveys
• A QSDS analysis can also be linked upstream to public administration surveys, and downstream to household surveys.
• Linking a QSDS with household surveys would allow to include the demand for services or outcomes.
• Benefit incidence analysis can be enhanced by using filter coefficients obtained from PETS/QSDS to deflate cost per user to take into
account leakage or capture of funds.
• Institutional and stakeholder analysis can help define the parameters of PETS surveys.
• Citizen Report Cards can serve as a monitoring tools to verify the perceived effectiveness of public services for stakeholders.
Key Elements:
The QSDS is typically implemented with the following steps: (1) Consultations with key stakeholders, including government agencies,
donors and civil society organizations are carried out to: define the objectives of the survey, identify the key issues, determine the
structure of resource flows and the institutional setup, review data availability, outline hypotheses and chose the appropriate survey tool.
(2) Survey instruments are then constructed and implemented. The QSDS deals with the fact that agents may have strong incentives to
misreport data by using a multi-angular data collection strategy and carefully considering which sources and respondents have incentives
to misreport, and identifying sources that tare the least contaminated by these incentives.
Requirements
Data/information:
In addition to the QSDS itself, uses public accounts sample data, preferably panel data, on government spending and information on
outputs of service providers at ministerial, regional, local and service provider levels. Field testing of the survey is key to ensuring high
quality results.
Time:
Consultations, design, and pre-testing take several months. The survey itself takes 1-2 months, depending on sample size and data
accessibility.
Skills:
Some prior experience of micro survey work and STATA required, and a detailed knowledge of the relevant institutional context.
Microeconomics of provider behavior (incentives and organization theory).
Supporting software: STATA or other similar software
Financial cost:
US$60-100,000 plus design
Limitations:
Results suffer from data limitations, i.e. where service provision is not well recorded, or is in-kind. Respondents may have incentives to
misreport information
References and applications:
• For an overview, see Dehn, Reinikka and Svensson (2003), Chapter 9 of the Toolkit for Evaluating the Poverty and Distributional
Impact of Economic Policies.
• Chaudhury and Hammer (2003) on Bangladesh.
• Lindelow et al. (2003) on Uganda
• See www.publicspending.org and http://econ.worldbank.org/programs/public_services/topic/tools/ for some of the tools
available and their applications.
78
Annex: Economic and Social Tools for Poverty and Social Impact Analysis
Tool Name:
Citizen Report Card
What is it?
A participatory survey that solicits client feedback on the performance of public services. It combines qualitative and quantitative methods
to collect useful demand side data that can help improve the performance of public services. Extensive media coverage and civil society
advocacy allows the tool to be used for public accountability.
What can it be used for?
Citizen Report Cards are used in situations where demand side data, such as user perceptions on quality and satisfaction with public
services, are absent. By collecting and aggregating user feedback they provide an avenue for citizens to signal public agencies and
politicians on key reform areas, and also to create competition among state-owned monopolies.
What does it tell you?
Citizen Report Cards provide feedback from actual users of services regarding issues such as:
(a) Availability of services,
(b) Satisfaction with services,
(c) Reliability/Quality of services and the indicators to measure these,
(d) Responsiveness of service providers,
(e) Hidden costs - corruption and support systems,
(f) Willingness to pay, and
(g) Quality of life.
Complementary tools:
• Can be used in conjunction with national service delivery and other household surveys. It is also being supported by the more
qualitative community scorecard process.
• Needs to be complemented with effective communications and publicity strategy to put information in the public domain and
increase effectiveness. The data from citizen report cards can also be used in conjunction with Public Expenditure Tracking Surveys
(example Sierra Leone) and Benefit Incidence Analysis.
Key Elements:
User-determined assessment criteria; quantitative feedback on service delivery quality; media involvement and broad public debate on
process and survey results.
Requirements
Data/information:
Being a survey itself, the only data/information required is for developing the sampling frame for the execution of the survey. For this
basic demographic, economic and social data from recent household surveys would be needed to decide on the stratification.
Time:
Between 3-6 months from the initial scoping to the dissemination stage. In most countries an initial orientation workshop for different
stakeholders is included.
Skills:
Usually the citizen report card is managed by a different agency from the on that actually executes the survey. For the latter, the norm
has been to out source to a market research agency such as ORG-MARG (India) or the Social Weather Station (Philippines), which has
adequate market research and statistical survey analysis skills. The managing organization is either an independent CSO with solid
advocacy skills, networks and reputation (India), an international donor like the World Bank (Philippines), or a Government Department
in charge of monitoring and independent review/oversight of public services.
Supporting software: N/A
Financial cost:
Varies according on the depth and purpose of analysis. A full national survey in a moderately sized country would cost in the range of
US$100,000.
Limitations:
The limitations include: (i) requires an agency with market research and data collection skills to conduct the survey, (ii) requires support
of media, (iii) role of expectations in user perceptions needs to be factored, (iv) limitations in comparability across services, (v) cost
considerations, (vi) large sample required for heterogeneous population and lesser used services, (vii) effort & time to stimulate action
by service agencies & civil society, (viii) lack of predictability in how different players respond.
References and applications:
• World Bank. (2002d).
• Public Affairs Center (2002).
79
A User’s Guide to Poverty and Social Impact Analysis
Tool Name:
Community Score Card
What is it?
A community based qualitative monitoring tool that draws on techniques of social audit, community monitoring and citizen report cards.
The process is also an instrument for empowerment and accountability as it includes an interface meeting between service providers and
the community that allows for immediate feedback.
What can it be used for?
The community scorecard is a tool for Participatory Public Expenditure Reviews. It is also used for local level monitoring and performance
evaluation of services, projects and even government administrative units (like district assemblies) by the community themselves.
The process allows for (a) tracking of inputs or expenditures (e.g. availability of drugs), (b) monitoring of the quality of
services/projects, (c) generation of benchmark performance criteria that can be used in resource allocation and budget decisions, (d)
comparison of performance across facilities/districts, (e) generating a direct feedback mechanism between providers and users, (f)
building local capacity and (g) strengthening citizen voice and community empowerment.
What does it tell you?
The community scorecard provides information on (a) how inputs or expenditures match with entitlements/allocations at the local/
facility level, (b) the criteria used by the community and by providers themselves to assess their own performance, (c) how both the
community and providers score themselves on these criteria, (d) anecdotal evidence on which these scores are based, and (e) how the
assessments by the community and providers can be used to develop an action plan for improvements in the project/service.
Complementary tools:
• Can be used in conjunction with national service delivery surveys and the citizen report card survey.
• Can form participatory component of public expenditure reviews, public expenditure tracking surveys, formal financial audits and
benefit incidence analyses.
Key Elements:
Community-based, i.e. designed and executed, qualitative service assessment; professionally facilitated public discussion of results.
Requirements
Data/information:
The input tracking scorecard requires supply side information on inputs and expenditures such as
(a) Budgets/allocations to a facility/project,
(b) Entitlements as specified under a policy/project document,
(c)List of inventories at facility level, etc.
At the community level, an existing social mapping, and basic demographic data is needed to divide the community into focus groups. If
the process is to be conducted on a representative sample of communities across the nation/district then basic socio-economic data would
be needed to select the sample frame.
Time:
The process itself requires only a few (sometimes a single) community gatherings. However the groundwork and orientation for collecting
supply-side input/expenditure data, preparing the providers and community for the scorecard and for the interface meeting, as well as
the secondary data analysis may require in the region of 3-6 weeks.
Skills:
The community scorecard process requires expert facilitation and so experience with participatory methods and a history of involvement
with the community are usually pre-requisites for the process to run smoothly.
Supporting software: N/A
Financial cost:
Financial costs of conducting the process in a single community are quite low - limited mainly to the time of the facilitating staff. If
however done on a large scale with many communities involved, the costs would be higher. Overall cost ranges from US$30,000 to
US$40,000, comparable to a beneficiary assessment.
Limitations:
The limitations of the community scorecard process include: (a) it depends a great deal on quality of facilitation, (b) input tracking
dependent on availability of supply side data, (c) the interface meeting can get confrontational, (d) greater standardization of indicators
needed when scaling up, (e) small sample size during gathering can bias results, (f) scoring not always applicable.
References and applications:
• Republic of Gambia (2002).
• Information can be found at: http://www.worldbank.org/participation/spaccount.htm
80
Bibliography
Note: The word processed describes informally reproduced works that may not be commonly available
through libraries.
Alderman, H. and C. del Ninno. 1999. “Poverty issues
for zero rating VAT in South Africa.” Journal of
African Economies 8 (2): 182-208.
Agénor, P. 2002. “Macroeconomic Adjustment and the
Poor: Analytical Issues and Cross-Country Evidence.” Staff Working Paper 2788. World Bank,
Washington, D.C.
Alwang, J., P. Siegel, and S. Jorgensen. 1996. “Seeking
Guidelines for Poverty Reduction in Rural Zambia.” World Development 24 (11): 1711–23.
Angrist, J. E. Bettinger, E. Bloom, E. King and M. Kremer. 2001. “Vouchers for Private Schooling in
Colombia: Evidence from a Randomized Natural
Experiment”, NBER Working Paper 8343.
Agénor, P., and J. Aizenman. 1999. “Macroeconomic
Adjustment with Segmented Labor Markets.”
Journal of Development Economics 58 (2): 277–96.
Arulpragasam, J. and P. Conway. 2003. “Partial Equilibrium Multi-Market Analysis.” In F. Bourguignon and L.A. Pereira da Silva, eds., Evaluating
the Poverty and Distributional Impact of Economic
Policies (Techniques and Tools), Washington D.C.:
World Bank
Agénor, P., A. Izquierdo, and H. Fofack. 2003.
“IMMPA: A Quantitative Macroeconomic Framework for the Analysis of Poverty Reduction Strategies.” World Bank, Washington, D.C. Processed.
Ahmad, E. and N. Stern. 1984. “The theory of reform
and Indian indirect taxes.” Journal of Public Economics 25 (3): 259-98.
Atkinson, A. and F. Bourguignon. 1991. “Tax-Benefit
Models for Developing Countries: Lessons from
Developed Countries” in J. Khalilzadeh-Shirazi
and A. Shah, eds., Tax Policy in Developing Countries, The World Bank, Washington, DC
Ahmad, E. and N. Stern. 1987. “Alternative sources of
government revenue: Illustrations from India,
1979-80.” In Newbery, David and Nicholas Stern,
eds. The Theory of Taxation for Developing Countries. Oxford: Oxford University Press.
Attanasio, O., C. Meghir and A. Santiago. 2002. “Education Choices in Mexico: Using a Structural
Model and a Randomized Experiment to Evaluate
Progresa”, University College London, processed.
Ahmad, E. and N. Stern. 1990. “Tax reform and
shadow prices for Pakistan.” Oxford Economic
Papers 42 (1): 135-59.
Baker, J., 2000. Evaluating the Impact of Development
Projects on Poverty: A Handbook for Practitioners.
Washington, D.C.: World Bank.
Ahmad, E. and N. Stern. 1991. The Theory and Practice
of Tax Reform in Developing Countries. Cambridge: Cambridge University Press.
81
A User’s Guide to Poverty and Social Impact Analysis
Programs: the Case of Bolsa Escola”, World Bank
Policy Research Working Paper #2916, Washington, DC.
Barnum, H., and L. Squire. 1979. A Model of an Agricultural Household: Theory and Evidence. Baltimore: Johns Hopkins University Press, for the
World Bank.
Bourguignon, F. and F. H.G. Ferreira. 2003. “Ex-Ante
Evaluation of Policy Reforms using Behavioral
Models.” In F. Bourguignon and L.A. Pereira da
Silva, eds., Evaluating the Poverty and Distributional Impact of Economic Policies (Techniques and
Tools), Washington D.C.: World Bank.
Barro, R. 1997. Determinants of Economic Growth: A
Cross-Country Empirical Study. Cambridge: MIT
Press.
Beck, C., G. Clarke, A. Groff., P. Keefer, and P. Walsh.
2002. “New Tools and New Tests in Comparative
Political Economy: The Database of Political Institutions”. World Bank. Washington, DC.
Bourguignon, F. and L.A. Pereira da Silva, eds. 2003.
Evaluating the Poverty and Distributional Impact of
Economic Policies (Techniques and Tools), Washington D.C.: World Bank
Becker, G. 1965. “A Theory of the Allocation of Time.”
Economic Journal 75: 493–517.
Brinkerhoff, D., and B. L. Crosby. 2002. Managing Policy Reform: Concepts and Tools for Decision-Makers
in Developing and Transition Countries. Bloomfield, Conn.: Kumarian Press.
Becker, H. 1997. Social Impact Assessment. London:
University College Press.
Benjamin, D. 1992. “Household Composition, Labor
Markets, and Labor Demand: Testing for Separation in Agricultural Household Models.” Econometrica 60: 287–322.
Carvalho, S., and H. White. 1997. “Combining the
Quantitative and Qualitative Approaches to
Poverty Measurement and Analysis: The Practice
and the Potential.” Technical Paper 366. World
Bank, Washington, D.C.
Bianchi, R., and S. Kossoudji. 2001. “Interest Groups
and Organizations as Stakeholders.” Social Development Paper 35. World Bank, Washington, D.C.
Castro-Leal, F. 1996. ‘Poverty and Inequality in the
Distribution of Public Education Spending in
South Africa.’ PSP Discussion Paper Series 102,
World Bank, Poverty and Social Policy Department, Washington D.C.
Binswanger, H., and J. Quizon. 1984. “Distributional
Consequences of Alternative Food Policies in India.”
Discussion Paper 20. World Bank, Agriculture and
Rural Development Department, Washington, D.C.
Castro-Leal, F., J. Dayton and L. Demery. 1997. ‘Public
Social Spending in Africa: Do the Poor Benefit?’
World Bank Research Observer
Binswanger, H. and J. Quizon 1986. “Modeling the
Impact of Agricultural Growth and Government
Policy on Income Distribution in India.” World
Bank Economic Review 1: 103–48.
Cernea, M. and A. Kudat, eds. 1997. Social Assessments
for Better Development, Case Studies in Russia and
Central Asia. ESSD Studies and Monograph
Series, no. 16. World Bank: Washington, DC.
Blundell, R., A. Duncan, J. McCrae and C. Meghir.
2000. “Evaluating In-Work Benefit Reforms: the
Working Families Tax Credit in the UK”. Discussion Paper, Institute for Fiscal Studies, London.
Chaudhury , N., and J. Hammer. 2003. “Ghost Doctors
: Absenteeism in Bangladeshi Health Facilities.”
World Bank. Processed
Bolt, R., and M. Fujimura. 2002. “Policy-based Lending and Poverty Reduction: An Overview of
Processes, Assessment, and Options.” Working
Paper Series 2. Asian Development Bank, Economics and Research Department, Manila.
Chen, D., J. Matovu and R. Reinikka. 2001. “A quest for
revenue and tax incidence.” In R. Reinikka and P.
Collier, eds., Uganda’s Recovery: The Role of Farms,
Firms and Government. Washington, D.C.: World
Bank.
Bourguignon, F., F.H.G. Ferreira and P. Leite. 2002.
“Ex-ante Evaluation of Conditional Cash Transfer
82
Bibliography
Decaluwé, B., J.-C. Dumont, and L. Savard. 1999.
“Measuring Poverty and Inequality in a Computable General Equilibrium Model.” Working
Paper 9920. Université Laval, Centre de Recherche
en Économie et Finance Appliquées, Montreal.
Christensen, L. R., D. W. Jorgensen, and L. J. Lau. 1975.
“Transcendental Logarithmic Utility Functions.”
American Economic Review 65: 367–83.
Coady, D., and R. Harris. 2001. “A Regional General
Equilibrium Analysis of the Welfare Impact of
Cash Transfers: An Analysis of Progresa in Mexico.” Trade and Macroeconomics Division Discussion Paper 76. International Food Policy Research
Institute. Washington, D.C.
Decaluwé, B., A. Patry, L. Savard, and E. Thorbecke.
1999. “Poverty Analysis within a General Equilibrium Framework.” Working Paper 9909. Université Laval, Centre de Recherche en Économie et
Finance Appliquées, Montreal.
Cornia, A., R. Jolly, and F. Stewart. 1987. Adjustment
with a Human Face. Oxford: Clarendon Press.
Dehn, J., R. Reinikka, and J. Svensson. 2003a. “Survey
tools for Assessing Performance in Service Delivery.” In F. Bourguignon and L.A. Pereira da Silva,
eds., Evaluating the Poverty and Distributional
Impact of Economic Policies (Techniques and Tools),
Washington D.C.: World Bank.
Cox, D., and E. Jimenez. 1995. “Private Transfers and
the Effectiveness of Public Income Redistribution
in the Philippines.” In D. Van de Walle and K.
Nead, eds., Public Spending and the Poor: Theory
and Evidence. Baltimore: Johns Hopkins University Press for the World Bank.
de Janvry, A., M. Fafchamps, and E. Sadoulet. 1991.
“Peasant Household Behaviour with Missing
Markets: Some Paradoxes Explained.” Economic
Journal 101: 1400–17.
Davidson, R., and J. Duclos. 2000. “Statistical Inference
for Stochastic Dominance and for the Measurement of Poverty and Inequality.” Econometrica 68
(6): 1435–64.
de Maio, L., F. Stewart, and R. van der Hoeven. 1999.
“Computable General Equilibrium Models,
Adjustment and the Poor in Africa.” World Development 27: 453–75.
Das, J., S. Dercon, J. Habyarimana, and P. Krishnan.
2002. “Rules vs. Discretion: Public and Private
Funding in Zambian Basic Education. Part I:
Funding Equity.” World Bank, Washington, D.C.
Demery, L. 2003. “Analyzing the Incidence of Public
Spending.” In F. Bourguignon and L.A. Pereira da
Silva, eds., Evaluating the Poverty and Distributional Impact of Economic Policies (Techniques and
Tools), Washington D.C.: World Bank.
Datt, G., K. Ramadas, D. van der Mensbrugghe, T.
Walker, and Q. Wodon. 2003. “Predicting the
effect of aggregate growth on poverty.” In F. Bourguignon and L.A. Pereira da Silva, eds., Evaluating
the Poverty and Distributional Impact of Economic
Policies (Techniques and Tools), Washington D.C.:
World Bank.
Demery, L. 2000. “Benefit Incidence: A Practitioner’s
Guide.” World Bank, Africa Region, Poverty and
Social Development Group, Washington, D.C.
Demery, L., S. Chao, R. Bernier and K. Mehra. 1995.
“The Incidence of Social Spending in Ghana”. PSP
Discussion Papers Series 82. Poverty and Social
Policy Department. World Bank, Washington,
D.C.
Datt, G. and T. Walker. 2002. PovStat 2.12, A Poverty
Projection Toolkit, User’s Manual, World Bank,
Washington, D.C. Processed.
Davidson, R., and J. Duclos. 2000. “Statistical Inference
for Stochastic Dominance and for the Measurement of Poverty and Inequality.” Econometrica 68
(6): 1435–64.
Demery, L., M. Ferroni, and C. Grootaert, eds. 1993.
Understanding the Social Effects of Policy Reform.
Washington, D.C.: World Bank.
Deaton, A., and J. Muellbauer. 1986. Economics and
Consumer Behavior. New York: Cambridge University Press.
Demombynes, G., C. Elbers, J. O. Lanjouw, P. Lanjouw,
J. A. Mistiaen, and B. Özler. 2002. ‘Producing an
83
A User’s Guide to Poverty and Social Impact Analysis
Improved Geographic Profile of Poverty: Methodology and Evidence from Three Developing
Countries’ Discussion Paper 2002/39, WIDER,
Helsinki.
Easterly, W. 1999. “The Ghost of Financing Gap: Testing the Growth Model Used in the International
Financial Institutions.” Journal of Development
Economics 60: 423–38.
Dervis, K., J. de Melo, and S. Robinson.1982. General
Equilibrium Models for Development Policy. New
York: Cambridge University Press.
Egamberdi, N., P. Gordon, A. Ikhamov, D. Kandiyoti,
and J. Shoerberlein-Engel. 2000. “Uzbekistan
Agriculture Enterprise Restructuring and Development Program.” In A. Kudat, C. Keyder, and S.
Peabody, eds., Social Assessment and Agricultural
Reform in Central Asia and Turkey. Washington,
D.C.: World Bank.
Devarajan, S. and D. S. Go. 2003. “The 123PRSP
Model.” In F. Bourguignon and L.A. Pereira da
Silva, eds., Evaluating the Poverty and Distributional Impact of Economic Policies (Techniques and
Tools), Washington D.C.: World Bank.
Elbers, C., J.O. Lanjouw, and P. Lanjouw. 2002. “Welfare in Villages and Towns: Micro level Estimation
of Poverty and Inequality”, Policy Research Working Paper 2911. World Bank, Washington, D.C.
Devarajan, S., and D. Go (with F. Charlier, A. Dabalen,
W. Easterly, H. Fofack, A. Izquierdo, and L.
Koryukin). 2001. “A Macroeconomic Framework
for Poverty Reduction Strategy Papers, with an
Application to Zambia.” World Bank, Washington,
D.C. Processed.
Elbers, C., J.O. Lanjouw, P. Lanjouw, and P.G. Leite.
2002. “Poverty and Inequality in Brazil: New Estimates from Combined PPV-PNAD Data”. World
Bank, Washington, D.C. Processed.
Devarajan, S., W. Easterly, H. Fofack, D. Go, A.
Izquierdo, C. Petersen, L. Pizzati, C. Scott, and L.
Serven. 2000. “A Macroeconomic Framework for
Poverty Reduction Strategies.” World Bank, Washington, D.C. Processed.
Elbers, C., P. Lanjouw, J. A. Mistiaen, B. Özler, and K.
Simler. 2002. “Are Neighbours Equal? Estimating
Local Inequality in Three Developing Countries”
paper presented at the LSE/Cornell/WIDER conference on Spatial Distribution of Inequality, London School of Economics, London.
Devarajan, S., H. Ghanem, and K. Thierfelder. 1999.
“Labor Market Regulations, Trade Liberalization,
and the Distribution of Income in Bangladesh.”
Journal of Policy Reform 3 (1): 1–28.
European Commission. 2002. “Project Cycle Management Handbook.” EuropeAid Cooperation Office,
General Affairs, Evaluation. Brussels.
Devarajan, S., and S. Hossain. 1998. “The Combined
Incidence of Taxes and Public Expenditures in the
Philippines.” World Development 26: 963–77.
Finsterbusch, K., J. Ingersoll, and L. Llewellyn. 1990.
Methods for Social Analysis in Developing Countries. San Francisco: Westview Press.
Dollar, D., and A. Kraay. 2002. “Growth Is Good for the
Poor.” Journal of Economic Growth Vol 7, Number
3, 195-225.
Foster, V., and C. Araujo. 2001. “Does Infrastructure
Reform Work for the Poor? A Case Study from
Guatemala.” World Bank, Washington, D.C.
Processed.
Dorosh, P., C. del Ninno and D. Sahn. 1995. “Poverty
Alleviation in Mozambique: a multi-market
analysis of the role of food aid”, Agicultural Economics 13, 89-99.
Galasso, E., M. Ravallion and A. Salvia. 2001. “Assisting
the Transition from Workfare to Work: A Randomized Experiment”. Policy Research Working
Paper 2738, World Bank, Washington, D.C.
Dulamdary, E., M. Shah, and R. Mearns, with B.
Enkhbat and L. Ganzaya. 2001. “Mongolia: Participatory Living Standards Assessment.” National
Statistics Office of Mongolia and The World Bank,
Washington, D.C.
Gelbach, J., and L. Pritchett. 2000. “Indicator Targeting
in a Political Economy: Leakier Can Be Better.”
Journal of Policy Reform 4 (2): 113–45.
84
Bibliography
International Centres for Economics and Related
Disciplines, London.
GTZ (Gesellschaft für Technische Zusammenarbeit).
1991. Methods and Instruments for Project Planning and Implementation. Eschborn, Germany.
Hunt, D. 1996. Process Mapping: How to Reengineer Your
Business Processes. New York: John Wiley & Sons.
Gibson, J. 1998. “Indirect tax reform and the poor in
Papua New Guinea.” Pacific Economic Bulletin 13
(2): 29-39.
Ianchovichina, E., A. Nicita, and I. Soloaga. 2001.
“Trade Reform and Household Welfare: The Case
of Mexico.” Working Paper 2667. World Bank,
Washington, D.C.
Gittinger, J. P. 1985. Economic Analysis of Agricultural
Projects. Washington, D.C.: World Bank.
Ironmonger, D. 1999. “An Overview of Time Use Surveys.” Paper presented at the International Seminar on Time Use Studies, Ahmedabad, India,
December 7–10 .
Goldman, L.R., ed. 2000. Social Impact Analysis: An
Applied Anthropology Manual. Oxford: Berg Press.
Government of Tanzania. 1999. “Tanzania Public
Expenditure Review: Health and Education Financial Tracking Study. Final report, Vol. III.” Price
Waterhouse Coopers. Dar es Salaam. Processed.
Jabara, C., M. Lundberg, and A. Sireh Jallow. 1992.
“Social Accounting Matrix for The Gambia.”
Working Paper No. 20. Cornell University, Cornell
Food and Nutrition Policy Program, Ithaca, N.Y.
Government of Tanzania. 2001. “ProPoor Expenditure
Tracking.” Research on Poverty Alleviation and
Economic (REPOA) and Social Research Foundation to Tanzania PER Working Group. Dar es
Salaam. Processed.
Jalan, J. and M. Ravallion. 2003a. “Estimating Benefits
Incidence for Anti-poverty Program using
Propensity Score Matching”. Journal of Business
and Economic Statistics, Volume 21, Issue 1.
Grootaert, C., and T. van Bastelaer, eds. 2002. Understanding and Measuring Social Capital: A Multidisciplinary Tool for Practitioners. Washington, D.C.:
World Bank.
Jalan, J. and M. Ravallion. 2003b. “Does Piped Water
Reduce Diarrhea for Children in Rural India?”
Journal of Econometrics, Volume 112, Issue 1.
Hammer, J., and A. Tan. 1989. “A Multimarket Model
for Turkish Agriculture.” Working Paper Series
285. World Bank, Agriculture and Rural Development Department, Washington, D.C.
Jalan, J. and M. Ravallion. 1999. “Are the Poor Less
Well Insured? Evidence on Vulnerability to
Income Risk in Rural China.” Journal of Development Economics 58 (1): 61–81.
Hammer, J., I. Nabi, and J. Cercone. 1995. “Distributional Effects of Social Sector Expenditures in
Malaysia.” In D. Van de Walle and K. Nead, eds.,
Public Spending and the Poor: Theory and Evidence. Baltimore: Johns Hopkins University Press
for the World Bank.
Jalan, J., and M. Ravallion. 1997. “Spatial Poverty
Traps?” Policy Research Working Paper 1862.
World Bank, Washington, D.C.
Jensen, R. 1998. “Public Transfers, Private Transfers,
and the Crowding-Out Hypothesis: Evidence
from South Africa.” Research Working Paper
R98–08. Harvard University, Kennedy School of
Government, Cambridge, Mass.
Haney, M., M. Shkaratan, V. Kabalina, V. Paniotto, and
C. Rughinis. 2003. “Mine Closure and Its Impact
on the Community: Five Years after Mine Closure
in Romania, Russia and Ukraine.” World Bank,
Washington, D.C. Forthcoming.
Kahane, A. 1996. “The Mont Fleur Scenarios: What
will South Africa be like in the year 2002?” Deeper
News. Volume 7, Number 1. Emeryville, CA.
Howes, S. 1993. “Mixed Dominance: A New Criterion
for Poverty Analysis.” Working Paper DARP/2.
London School of Economics, Suntory and Toyota
Kahane, A. 1998. “Destino Colombia: A ScenarioPlanning Process for the New Millenium”. Deeper
News. Volume 9, Number 1. Emeryville, CA.
85
A User’s Guide to Poverty and Social Impact Analysis
Lofgren, H., S. Robinson and M. El-Said. 2003.
“Poverty and Inequality Analysis in a General
Equilibrium Framework: The Representative
Household Approach.” In F. Bourguignon and L.A.
Pereira da Silva, eds., Evaluating the Poverty and
Distributional Impact of Economic Policies (Techniques and Tools), Washington D.C.: World Bank.
Kaufman, D. and A. Kraay. 2000. “Governance Matters
II: Updated Indicators for 2000-01”.Working
Paper No 2772. World Bank, Washington, D.C.
Lampietti, J., A. Kolb, S. Gulyani, and V. Avenesyan.
2001. “Utility Pricing and the Poor: Lessons from
Armenia.” Technical Paper 497. World Bank,
Washington, D.C.
Lanjouw, P. 2003. “Estimating Geographically Disaggregated Welfare Levels and Changes.” In F. Bourguignon and L.A. Pereira da Silva, eds., Evaluating
the Poverty and Distributional Impact of Economic
Policies (Techniques and Tools), Washington D.C.:
World Bank.
López, R. 1986. “Structural Models of the Farm
Household that Allow for Interdependent Utility
and Profit-Maximization Decisions.” In I. Singh,
L. Squire, and J. Strauss, eds., Agricultural Household Models: Extensions, Applications, and Policy.
Baltimore: Johns Hopkins University Press for the
World Bank.
Lanjouw, P., M. Pradhan, F. Saadah, H. Sayed, and R.
Sparrow. 2001. “Poverty, Education, and Health in
Indonesia: Who Benefits from Public Spending?”
Policy Research Working Paper 2739. World Bank,
Washington, D.C.
López, R., J. Nash, and J. Stanton. 1995. “Adjustment and
Poverty in Mexican Agriculture: How Farmers’
Wealth Affects Supply Response.” Policy Research
Working Paper 1494. World Bank, Washington, D.C.
Lanjouw, P. and M. Ravallion. 1999. “Benefit Incidence
and the Timing of Program Capture,” World Bank
Economic Review. 13 (2) : 257-274.
Lundberg, M., M. Over, and P. Mujinja. 2000. “Sources
of Financial Assistance for Households Suffering
an Adult Death in Kagera.” Policy Research Working Paper 2508. World Bank, Washington, D.C.
Lanjouw, P., and M. Ravallion. 1995. “Poverty and
Household Size.” Economic Journal 105: 1415–34.
Maack, J. 2001. “Scenario Analysis: A Tool for Task
Managers.” In R. Krueger, M. Casey, J. Donner, S.
Kirsch, and J. Maack, “Social Analysis: Selected
Tools and Techniques.” Social Development
Papers 36. World Bank, Social Development
Department, Washington, D.C.
Levinsohn, J., S. Berry, and J. Friedman. 1999. “Impacts
of the Indonesian Economic Crisis: Price Changes
and the Poor.” Working Paper 7194. National
Bureau of Economic Research, Cambridge, Mass.
Minot, N. and F. Goletti. 1998. “Export Liberalization
and Household Welfare: The Case of Rice in Vietnam.” American Journal of Agricultural Economics
80 3: 738-749
Lindelöw, M., R. Reinikka, and J. Svensson. 2003.
“Health Care on the Frontline: Survey Evidence
on Public and Private Providers in Uganda.”
Human Development Working Paper Series,
World Bank, Africa Region,Washington D.C.
Forthcoming.
Mistiaen, J.A. 2002. “Small Area Estimates of Welfare
Impacts: The Case of Food Price Changes in
Madagascar”, World Bank. Washington D.C.
Processed
Lockhart, C. 2001. “Institutional Analysis: Russia Coal
Case Study.” World Bank, Social Development
Department, Washington, D.C. Processed.
Mistiaen, J.A., Özler, B., Razafimanantena, T., and
Razafindravonona, J. 2002. “Putting Welfare on
the Map in Madagascar”. World Bank. Washington
D.C. Processed
Lofgren, H., R. Harris, and S. Robinson with assistance
from M. Thomas and M. El-Said. 2002. “A Standard Computable General Equilibrium (CGE)
Model in GAMS.” Microcomputers in Policy
Research, Vol. 5. International Food Policy
Research Institute. Washington, D.C.
Narayan, D., and P. Petesch. 2002. “An Empowering
Approach to Poverty Reduction.” In Voices of the
86
Bibliography
Poor: From Many Lands. New York: Oxford University Press for the World Bank.
Papanek, G. 1994. The Social Impact of Program Lending. Manila: Asian Development Bank.
National Management Consultants. 2000. “Second
Annual Report of the Kecamatan Development
Project.” Jakarta. Processed.
Pruitt, B. 2000. UNDP Civic Scenario/Civic Dialogue
Workshop. Antigua, Guatemala, Nov 8-10, 2000.
New York: UNDP.
North, D. 1990. Institutions, Institutional Change and
Economic Performance. New York: Cambridge
University Press.
Public Affairs Center. 2002. The State of Karnataka’s
Public Services: Benchmarks for the New Millennium. Bangalore, India: State of Karnataka.
Norton, A., B. Bird, K. Brock, M. Kakande, and C.
Turk. 2001. A Rough Guide to PPAs: Participatory
Poverty Assessment—An Introduction to Theory
and Practice. London: ODI Publications.
Pyatt, G., and J. Round, eds. 1985. Social Accounting
Matrices: A Basis for Planning. Washington, D.C.:
World Bank.
Quah, D., and S. Durlauf. 1999. “The New Empirics of
Economic Growth.” In John Taylor and Michael
Woodford, ed., The Handbook of Macroeconomics.
Amsterdam: Elsevier Science Publishers.
Orbeta, A., and M. Alba. 1998. “Simulating the Impact
of Macroeconomic Policy Changes on the Nutritional Status of Households.” Micro Impacts of
Macroeconomic and Adjustment Policies
(MIMAP) Research Paper 21. International
Development Research Centre, Ottawa.
Radulescu, S., and M. Larionescu. 1999. “Social Assessment of Mining Restructuring in Romania.”
World Bank, Washington, D.C. Processed.
Papanek, G. 1994. The Social Impact of Program Lending. Manila: Asian Development Bank.
Rama, M. 2001. “The Gender Implications of Public
Sector Downsizing: The Reform Program of Vietnam.” Policy Research Working Paper 2573. World
Bank, Washington, D.C. Republished in World
Bank Research Observer 17 (2): 167–89.
Pereira da Silva, L.A., B. Essama-Nssah and I. Samaké.
2002. “A Poverty Analysis Macroeconomic Simulator (PAMS): Linking household surveys with
macro-models.” Working Paper 2888. World
Bank, Washington, D.C.
Ramadas, K. D. van der Mensbrugghe, and Q. Wodon
(2002) SimSip Poverty: Poverty and Inequality
Comparisons Using Group Data, Washington DC:
World Bank.
Pereira da Silva, L.A., B. Essama-Nssah and I. Samaké.
2003. “Linking Aggregate Macro-Consistency Models to Household Surveys: A Poverty Analysis Macroeconomoic Simulator (PAMS).” In F. Bourguignon
and L.A. Pereira da Silva, eds., Evaluating the Poverty
and Distributional Impact of Economic Policies (Techniques and Tools), Washington D.C.: World Bank.
Ravallion, M. 1999. “Monitoring Targeting Performance When Decentralized Allocations to the
Poor Are Unobserved.” Policy Research Working
Paper 2080. World Bank, Washington, D.C.
Ravallion, M. 1999. “Is More Targeting Consistent
with Less Spending?” International Tax and Public
Finance 6 : 411-19.
Powell M. and J. I. Round. 2000. ‘Structure and Linkage in the Economy of Ghana: A SAM Approach’,
in E Aryeetey, J Harrigan and M Nissanke, eds.
Economic Reforms in Ghana: Miracle or Mirage,
Oxford: James Currey Press
Ravallion, M. 2003. “Assessing the Poverty Impact of
an Assigned Program” In F. Bourguignon and L.A.
Pereira da Silva, eds., Evaluating the Poverty and
Distributional Impact of Economic Policies (Techniques and Tools), Washington D.C.: World Bank.
Powers, John. 2003. “The World Bank and Poverty and
Social Impact Analysis: Considering a Transaction
Cost Analysis Approach.” World Bank, Social
Development Department, Washington, D.C.
Forthcoming.
Ravallion, M. E. Galasso, T. Lazo and E. Philipp. 2001.
“Do Workfare Participants Recover Quickly from
87
A User’s Guide to Poverty and Social Impact Analysis
Retrenchment?” Policy Research Working Paper
2672, World Bank. Washington, D.C.
national Development Consultants Ltd. Kampala.
Processed.
Rao, V., and M. Woolcock. 2003. “Integrating Qualitative and Quantitative Approaches in Program
Evaluation.” In “Techniques and Tools for Evaluating the Poverty and Distributional Impact of Economic Policies.” Processed. World Bank,
Washington, D.C.
Rickson, R., J. Western, and R. Burge. 1990. “Social
Impact Assessment: Knowledge and Development.” Environmental Impact Assessment Review
10. World Bank, Washington, D.C.
Robb, C. 2002. Can the Poor Influence Policy? Participatory Poverty Assessments in the Developing World.
Washington, D.C.: World Bank.
Reinert, K. A. and D. W. Roland-Holst (1997) “Social
Accounting Matrices.” In J. F. Francois and K. A.
Reinert eds., Applied Methods for Trade Policy
Analysis: A Handbook, Cambridge: Cambridge
University Press
Robillard, A.-S., F. Bourguignon, and S. Robinson.
2001. “Crisis and Income Distribution: A MicroMacro Model for Indonesia.” International Food
Policy Research Institute, Washington, D.C.
Processed.
Reinikka, R. 2001. “Recovery in Service Delivery: Evidence from Schools and Health Centers.” In R.
Reinikka and P. Collier, eds., Uganda’s Recovery:
The Role of Farms, Firms and Government. World
Bank Regional and Sectoral Studies. Washington,
D.C.: World Bank
Robles, M., C. Siaens and Q. Wodon. 2003. “Poverty,
Inequality and Growth in Paraguay: Simulations
Using SimSIP Poverty.” Economia & Sociedad.
Forthcoming.
Rosenbaum, P. and D. Rubin, 1983. “The central role of
the propensity score in observational studies for
causal effects.” Biometrika. 70 : 41-55
Reinikka, R., and J. Svensson. 2002a. “Explaining Leakage of Public Funds.” Discussion Paper 3227. Centre for Economic Policy Research, London.
Reinikka, R. and J. Svensson. 2002b. “Working for
God? Evaluating Service Delivery of Religious
Not-for-Profit Health Care Providers in Uganda.”
World Bank, Development Research Group,
Washington, D.C. Processed.
Round, Jeffery,. 2003. “Social Accounting Matrices and
SAM-based Multiplier Analysis.” In F. Bourguignon and L.A. Pereira da Silva, eds., Evaluating
the Poverty and Distributional Impact of Economic
Policies (Techniques and Tools), Washington D.C.:
World Bank
Reinikka, R., and J. Svensson. 2003. “The Power of
Information: Evidence from an Information
Campaign to Reduce Capture.” World Bank.
Washington, D.C. Processed.
Rutherford, M. 1994. Institutions in Economics: The
Old and the New Institutionalism. New York: Cambridge University Press.
Sadoulet E and A de Janvry. 1995. Quantitative Development Policy Analysis. Baltimore: Johns Hopkins
University Press.
Republic of Gambia. 2002. “Strategy for Poverty Alleviation (SPAII) (PRSP).” Department of State for
Finance and Economic Affairs, Strategy for
Poverty Alleviation Coordinating Office. Banjul.
Processed.
Sahn, D., and H. Alderman. 1995. “Incentive Effects on
Labor Supply of Sri Lanka’s Rice Subsidy.” In D.
Van de Walle and K. Nead, eds., Public Spending
and the Poor: Theory and Evidence. Baltimore:
Johns Hopkins University Press for the World
Bank.
Republic of Uganda. 2000. “Tracking the Flow of and
Accountability for UPE Funds.” International
Development Consultants Ltd. Kampala.
Processed.
Sahn, D. E. and S. D. Younger. 2003. “Estimating the
Incidence of Indirect Taxes in Developing Coun-
Republic of Uganda. 2001. “Study to Track Use of and
Accountability of UPE Capitation Grants.” Inter88
Bibliography
tries.” In F. Bourguignon and L.A. Pereira da Silva,
eds., Evaluating the Poverty and Distributional
Impact of Economic Policies (Techniques and Tools),
Washington D.C.: World Bank
Tarp, F, D Roland-Holst and J Rand. 2002. “Trade and
Income Growth in Vietnam: Estimates from a
New Social Accounting Matrix.” Economic Systems
Research 14 (2): 157-184.
Salmen, Lawrence F. 2002. Beneficiary Assessment, An
Approach Described. Social Development Paper
No. 10.World Bank, Social Development Department, Washington, D.C.
Thorbecke, E., and H. Jung. 1996. “A Multiplier
Decomposition Method to Analyze Poverty Alleviation.” Journal of Development Economics 48 (2):
279–300.
Salmen, Lawrence F.1998. and Amelga, Misgana.
Implementing Beneficiary Assessment in Education:
A Guide for Practitioners (with examples from
Brazil). Social Development Paper No. 25. World
Bank, Social Development Department, Washington, D.C.
Timmer, C. P., W. Falcon, and S. Pearson. 1983. Food
Policy Analysis. Baltimore: Johns Hopkins University Press.
Torero, M., and A. Pascó-Font. 2001. “The Social
Impact of Privatization and the Regulation of
Utilities in Peru.” Discussion Paper 2001/17.
UNU-WIDER, Helsinki.
Salmen, Lawrence F. 1995. Participatory Poverty Assessment, Incorporating Poor People’s Perspectives into
Poverty Assessment Work. Social Development
Paper No. 11. World Bank, Social Development
Department, Washington, D.C.
Tymons, R.T., and Jacobs, R.A. 1997 “Multi-level
Process Mapping: A tool for Cross-Functional
Quality Analysis”, Production and Inventory Management Journal, 4th Quarter: 71-75.
Sechaba Consultants. 2002. Ability and Willingness to
Pay for Urban Water Supply. Maseru, Lesotho.
Processed.
van de Walle, D. 1992. “The Distribution of the Benefits from Social Services in Indonesia,1978-87.”
Policy Research Working Paper 871. World Bank,
Washington, D.C.
Shoven, J., and J. Whalley. 1992. Applying General
Equilibrium. New York: Cambridge University
Press.
van de Walle, D. 1994.“The Distribution of Subsidies
through Public Health Services in Indonesia 197887.” World Bank Economic Review 8 (2): 279-309.
Singh, I., L. Squire, and J. Strauss. 1986. Agricultural
Household Models: Extensions, Applications, and
Policy. Baltimore: Johns Hopkins University Press.
van de Walle, D. 1998. “Assessing the Welfare Impacts
of Public Spending.” World Development 26 (3):
365–79.
Squire, L., and H. van der Tak. 1975. Economic Analysis of Projects. Baltimore: Johns Hopkins University Press for the World Bank.
van de Walle, D.. 2002a. “Choosing Rural Road Investments to Help Reduce Poverty.” World Development 30 (4): 575–89.
Stone, R. 1954. “Linear Expenditure Systems and
Demand Analysis: An Application to the Pattern
of British Demand.” Economic Journal 64: 511–27.
van de Walle, D. 2002b, “The Static and Dynamic Incidence of Viet Nam’s Public Safety Net,” Policy
Research Working Paper 2791. World Bank, Washington, D.C.
Strauss, J. 1984. “Joint Determination of Food Consumption and Production in Rural Sierra Leone:
Estimates of a Household-Firm Model.” Journal of
Development Economics 14: 77–103.
van de Walle, D. 2002c. “Poverty and Transfers in
Yemen”. Middle East and North Africa Working
Paper 30, World Bank, Washington, DC.
Subramanian, S., and A. Deaton. 1996. “The Demand
for Food and Calories.” Journal of Political Economy 104: 133–62.
van de Walle, D. 2003. “Behavioral Incidence Analysis
of Public Spending and Social Programs.” In F.
89
A User’s Guide to Poverty and Social Impact Analysis
World Bank. 2002c. Social Analysis Sourcebook: Incorporating Social Dimensions into Bank-Supported Projects.
Social Development Department, Washington, D.C.
Bourguignon and L.A. Pereira da Silva, eds., Evaluating the Poverty and Distributional Impact of
Economic Policies (Techniques and Tools), Washington D.C.: World Bank.
World Bank. 2002d. Filipino Report Card on Pro-Poor
Services: Summary, ESSD Unit, East Asia and
Pacific Region, 2002. Washington D.C. Processed.
Wodon, Q., K. Ramadas and D. van der Mensbrughghe. 2003. SimSIP Poverty Module, World
Bank. Washington, D.C.
World Bank. 2002e. “Program Document for a Poverty
Reduction Support Credit in the amount equivalent to SDR 9.1 million to the Cooperative Republic of Guyana.” Washington D.C. Processed.
World Bank. 2000a. World Development Report
2000/2001: Attacking Poverty. New York: Oxford
University Press.
Xiao, Y., and S. Canagarajah. 2002. “Efficiency of Public Expenditure Distribution and Beyond: A
Report on Ghana’s 2000 Public Expenditure
Tracking Survey in the Sectors of Primary Health
and Education.” Africa Region Working Paper
Series 31. World Bank, Washington, D.C.
World Bank. 2000b. “Modeling Pensions Reform: The
World Bank’s Pension Reform Options Simulation Toolkit.” Human Development Network,
Social Promotion, Washington, D.C.
World Bank. 2001a. “Malawi Public Expenditures:
Issues and Options.” Report 22440 MAI. Washington, D.C.
Yitzhaki, S., and J. Slemrod. 1991. “Welfare Dominance: An Application to Commodity Taxation.”
American Economic Review 81 (3): 480–96.
World Bank. 2001b. “Honduras: Public Expenditure
Management for Poverty Reduction and Fiscal
Sustainability.” Report 22070. Poverty Reduction
and Economic Sector Management Unit, Washington, D.C.
Younger, S. 1993. “Estimating Tax Incidence in Ghana:
An Exercise using Household Data.” Cornell Food
and Nutrition Policy Program Working Paper 48.
Ithaca. Processed.
World Bank. 2002a. “Monitoring and Evaluation:
Some Tools, Methods, and Approaches.” Operations Evaluation Department, Washington, D.C.
Younger, S., D.E. Sahn, S. Haggblade, and P.A. Dorosh.
1999. “Tax Incidence in Madagascar: An Analysis
Using Household Data.” World Bank Economic
Review 13 : 303-331.
World Bank. 2002b. “Bosnia and Herzegovina Local
Level Institutions and Social Capital Study: Findings and Recommendations.” Europe and Central
Asia, Environment and Socially Sustainable
Development Department, Washington, D.C.
Processed.
Younger, S. 2002. “Benefits on the Margin: Observations on Marginal Benefit Incidence”. Food and
Nutrition Policy Program. Cornell University.
Ithaca, NY. Processed.
90
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertisement