Transcript Poverty and Inequality Analysis in a Standard CGE

Economy-Wide Models and
Poverty Analysis
Sherman Robinson, IDS, Sussex
Hans Lofgren, World Bank, Washington, D.C.
Presentation at the WIDER Conference
“Frontiers of Poverty Analysis,” held in
Helsinki, Finland, September 26-27, 2008
Introduction
• Policy Issues: Poverty and MDGs
• Economy-Wide framework
– Link development strategy choices with poverty
and MDG outcomes
– Top-down versus bottom-up approaches
• Analytic and empirical models
– CGE models with representative households
– Households and microsimulation models
– MDG links
2
What do we want to capture?
Macroeconomic
Environment
Structural features
Binding macro constraints
General Equilibrium effects
Factor markets
Factor market functioning
Segmentation
Wage determination
Households
Heterogeneity
Human and physical capital
Demographic Composition
Preferences
Access to Markets
3
Typical model structure
Costs
Returns
Factor
markets
Domestic private savings
Taxes
Activities
Sales
Households
Intermed.
demand
Sav. / Invest.
Transfers
Commodity
markets
Imports
Government
Private cons.
Exports
Invest.
demand
Gov. cons.
Foreign transfers
Foreign savings
Rest of world
Data requirements
Household surveys (LSMS)
National accounts (RMSM)
Trade data (Cust. & Excise)
Labor force surveys
+
B.o.P. (RMSM)
Supply/use table
=
Social Accounting
Matrix (SAM)
(already available
for many countries)
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A National SAM
Expenditures
Receipts
Activity
Activity
Commodity
Institutions
Hshlds
World
Total sales
Commodity
Intermediate
inputs
Factors
Value added
Institutions
Indirect taxes
Final
demand
Indirect taxes
and tariffs
Hshlds
World
Totals
Factors
Consumption Exports
Factor
income
Saving
taxes
Saving
taxes
Factor
income
Payments
to hshlds
transfers
remittances
Hshld
income
Foreign
Exchange
inflow
Inflows
Imports
Total costs
Total supply
Factor
income
Institution
income
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CGE Models
• CGE models are widely applied to policy
analysis both in developed and in developing
countries.
– Many applications in trade policy
• “Standard” static and dynamic models
– Various approaches to incorporating
distributional features: poverty and MDGs
6
Modeling Impact of “shocks” on
poverty and income distribution
• “Shocks” include:
– Macro shocks and structural adjustment
– Trade reform: country, regional, global
– MDG development strategies
• Most existing studies of the distributive
effects of shocks rely either on:
– comparison of distributions before and after the shock,
– counterfactuals based on macro models with some
disaggregation of the household sector.
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Counterfactual Analysis
• Links between policy changes and impacts
– Single policies and strategies (e.g., MDGs)
• Decomposing the impact of shocks
– Exogenous shocks (e.g., Asian crisis)
– Policy responses
• Historical analysis: analyzing causes
– Turkey 1972-77: Dervis, de Melo, and Robinson
– Model as a measurement device
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Poverty Analysis
• Two basic approaches
– Representative households in country models
• Summary representation of income distribution within
household groups
– Microsimulation models at household level
• Separate, linked, or integrated with economy-wide
models
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Representative Households
• Standard approach in early income-distribution focused CGE
models. (Adelman-Robinson. Recent: Decaluwe et al.)
– First, classify households into “representative” groups.
– Second, assume that the relative within-group income distribution for
each representative group does not change, given the shocks being
analyzed.
• CGE model generates changes in group mean incomes.
– Distributional statistics generated by aggregating within-group
distributions
– Generate standard distribution/poverty measures
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Representative Households
• Linking activities, factor incomes, households
– Functional distribution
– Extended functional distribution
– Livelihood strategies by households
• Household disaggregation
– Within-group distributions not affected, so model
cannot explain or affect much poverty/inequality
– Representative to full household surveys
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Microsimulation
“... instead of aggregating observations within a
household survey into a few household groups in
conformity with the requirements of CGE-type
models, our aim should be to work directly with all
the individual observations of the survey. By doing
so, we hope to achieve full consistency between
macroeconomic reasoning and standard poverty
evaluation.” Bourguignon, 1999.
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Microsimulation
• Integrated CGE - microsimulation model
• Top-down approach
• sequential approach with CGE model feeding
microsimulation with price and income data
• No feedback from micro to macro levels
• Different degrees of complexity at the
microsimulation level
– Models of household behavior
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Simple Top-Down Approach
• Link model results to a household survey.
– Survey households are classified the same as
representative households in CGE model.
– CGE model generates incomes and prices
– Individual survey observations scaled using
simulated changes in representative household
income and prices.
– Distributional measures computed from adjusted
survey.
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Microsimulation
• The essence of microsimulation is to model
the behavior of individual agents
(households or firms) that are included in a
micro database.
• In order to extend the analysis from partialequilibrium issues, microsimulation models
can be linked to CGE models.
– Potential to link economy wide shocks to
household outcomes
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The Sequential Framework
Macro-level module (Extended CGE-type model)
- Occupational structure:
L
- Price variables:
p
- Wage and earnings:
w
- All other variables in macro module:
Y
Link variables: L, w
Micro-simulation module (Household survey)
- Socio demographic characteristics: Si
- Occupational/labor-supply choice:
- Income:
li = O(Si,)
yi = E(Si,).li
Consistency with macro. Find changes in parameters 
and  such that:
li = L and Mean E(Si,) = w
Outcome = change in distribution of income
conditionally on characteristics S.
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UNDP Latin America Studies
• UNDP Projects on Latin America:
– Trade: 16 country studies
• Top-down CGE - microsimulation
– CGE models with focus on international trade
• Various degrees of household disaggregation
– Income, employment, and prices sent down
– Limited behavior at household level
• Latin American school of microsimulation
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UNDP Latin America Studies
• UN Projects on Latin America:
– Trade – with IFPRI, 16 country studies
– MDG Strategies – with World Bank; 18 country studies
• Top-down CGE - microsimulation
– CGE models with focus on international trade
• Various degrees of household disaggregation
– Income, employment, and prices sent down
– Limited behavior at household level
• Latin American school of microsimulation
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The Integrated Framework
Macro-level module (Extended CGE-type model)
- Occupational structure:
L
- Price variables:
p
- Wage and earnings:
w
- All other variables in macro module:
Y
Micro-simulation module (Household survey)
- Socio demographic characteristics: Si
- Occupational/labor-supply choice:
- Income:
li = O(Si,E)
yi = E(Si,w).li
E = earning rate of individual/household i in various
occupations. These “personal” rates are a function of a
set of standard market rates, w.
Outcome = change in distribution of income
conditionally on characteristics S.
Aggregating:
li = L
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Microsimulation vs RH models
• Comparing RH models and microsimulation
– When does disaggregation matter?
• Household impact and behavior
– If “shocks” affects variables such as prices or
average wages, RH models do fine
– If “shocks” affect employment or discontinuous
household behavior, microsimulation matters
• Labor participation
• Distinction is not sharp: continuum of models
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MAMS – Maquette for MDG Simulations
•
Dynamic-recursive CGE Model for MDG analysis;
developed at World Bank
–
–
–
•
•
•
Initial motivation: need to address country-level MDG strategies:
How can government policies, with foreign aid providing part of
the financing, be designed for achievement of the MDGs?
Evolved into general framework for country-level, medium-to-longrun development policy analysis, with emphasis on fiscal issues
and MDGs.
Different versions (differing in data needs and issues they can
address) ranging from aggregated macro to disaggregated MDG.
Starting point: standard dynamic-recursive CGE model
Main innovation: covers the generation of MDG and
education outcomes.
MAMS has been used with the standard approaches to
poverty and inequality analysis.
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MAMS
•
Applications in many countries:
–
–
–
•
18 in Latin America and the Caribbean
9 in Sub-Saharan Africa
5 in MENA region
Used in the context World Bank country analysis
(including Country Economic Memoranda, Public
Expenditure Reviews, Poverty Assessments) as
well as in joint work with the UN (UN-DESA and
UNDP) on Latin America and the MENA region.
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Issues in MDG strategy analysis
•
A framework for analysis of MDG strategies
should consider the following factors:
1. Synergies between different MDGs
2. Role of non-government service providers
3. Demand-side conditions (incentives, infrastructure,
incomes)
4. Role of economic growth
5. Macro consequences of increased government
spending under different financing scenarios
6. Diminishing marginal returns (in terms of MDG
indicators) to services and other determinants
7. Role of efficiency and input prices (e.g. wages) in
determining unit service costs
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MAMS: Model Structure
•
An extended, dynamic-recursive computable general
equilibrium (CGE) model designed for MDG analysis.
•
Complementary to and draws extensively on sector and
econometric research on MDGs.
•
Motivation behind the design of MAMS:
–
An economywide, flexible-price model is required for
development strategy analysis.
–
Standard CGE models provide a good starting point.
–
But standard CGE approach must be complemented by a
satisfactory representation of “social” sectors.
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2. Model Structure
MAMS: Model Structure
•
•
Extended to capture the generation of MDG outcomes.
MAMS covers MDGs 1 (poverty), 2 (primary school
completion), 4 (under-five mortality rate), 5 (maternal
mortality rate), 7a (water access), and 7b (sanitation
access).
•
The main originality of MAMS compared to standard CGE
models is the inclusion of (MDG-related) social services
and their impact on the rest of the economy.
•
Social services may be produced by the government and
the private sector.
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2. Model Structure
MAMS: Role of Government
•
Government services are produced using labor, capital, and intermediates
(fixed coefficients for capital, intermediate inputs, and aggregate labor; flexible
coefficients for disaggregated labor).
•
Government spending is split into
– Recurrent: consumption, transfers, interest
– Capital (investment)
•
Government demand (consumption and investment) is classified by function:
social services (education, health, water-sanitation), infrastructure and “other
government”.
•
Government spending is financed by taxes, domestic borrowing, foreign
borrowing, and foreign grants.
•
Model tracks government domestic and foreign debt stocks (including foreign
debt relief) and related interest payments.
•
Simplified versions of equations for government recurrent receipts, recurrent
expenditure, savings, and investment expenditure…..
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2. Model Structure
MAMS: MDG “production”
•
•
Together with other determinants, government social
services determine the "production" of MDGs.
MDGs are modeled as being “produced” by a combination
of factors or determinants (table following) using a
(reduced) functional form that permits:
– Imposition of limits (maximum or minimum) given by logic or
country experiences
– Replication of base-year values and elasticities
– Calibration of a reference time path for achieving MDGs
– Diminishing marginal returns to the inputs
•
Two-level function:
1. Constant-elasticity function at the bottom: Z = f(X)
2. Logistic function at the top: MDG = g(Z)
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MAMS: Data Requirements
• Core needs are similar to other CGE models:
– Social Accounting Matrix (SAM); stocks of factors,
population, and debts (foreign and domestic); elasticities
in trade, production, and consumption;
• They depend on the (flexible) disaggregation of the model.
• The SAM is used to define most of these parameters.
• Requirements specific to MDG version:
– In SAM: government consumption and investment
disaggregated by MDG-related functions; labor
disaggregated by educational achievement;
– Education parameters: stocks of students by educational
cycle; student behavioral patterns (ex: rates of passing,
repetition, dropout); population data with some
disaggregation by age;
– MDG data: indicators for base-year and 1990; elasticities;
calibration scenario for achieving each MDG.
MAMS: MDG Scenarios
• The BASE scenario is a “business-as-usual”
continues that may have the following
characteristics:
– Growth in GDP reflects trend of last 5-15 years.
– Unchanged GDP shares for government demand, foreign
aid, and debt stocks.
– Other policies are unchanged or adjusted according to
trends.
– Other exogenous items grow at the same rate as GDP.
• The BASE scenario serves as a benchmark for
comparisons.
Examples of MDG Scenarios
•
Questions commonly addressed by non-BASE
scenarios: What happens if the government …
1. expands services sufficiently to reach the MDGs with
additional financing provided by (a) foreign grants; (b)
domestic taxes; (c) domestic borrowing?
2. contracts in one area (e.g. human development or
other government) and expands in another (e.g.
infrastructure) with unchanged aid and domestic
policies?
3. expands in one area with additional financing from a, b,
or c (as defined under 1)?
4. becomes more/less productive, adjusting one or more
types of spending or financing in response?
MAMS: Ethiopia Study
• BASE (as described above)
• MDG-BASE (core MDG scenario):
– Government service growth is sufficient to
achieve all HD MDGs (2, 4, 5, 7a, 7b) by 2015
– Foreign grants are unconstrained; adjust to
meet the government financing gap
• Various simulations exploring tradeoffs
among MDGs and issues of timing and
costs.
Ethiopia: MDG Values
1990
2002
goal: 2015
38.4
36.2
19.2
MDG 1
headcount poverty rate (%)
MDG 2
primary (1st cycle) net completion rate (%)
24
36
100
MDG 4
under-five mortality rate (per 1,000 live births)
204
163
68
MDG 5
maternal mortality rate (per 100,000 live births)
870
609
218
MDG 7a
access to safe drinking water (%)
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24.4
62.5
MDG 7b
access to safe sanitation (%)
8
12
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Key Ethiopia findings
• Foreign aid per capita increases five-fold to
US$79 in 2015 as compared to 2005.
• Heavy reliance on foreign aid appreciates the
real exchange rate appreciation and skews
production toward non-tradables.
• In the educated part of the labor market, wage
increases are initially rapid but will later slow
down when labor supplies increase and the
scaling-up period is concluded.
• Relative to an emphasis on infrastructure, a
human development focus puts the economy on
a slower growth track.
Poverty Analysis: The Road Ahead
• Improving microeconomic specifications
– Intertemporal household behavior
• savings and investment: physical and human capital
• demographic changes and migration
– Intra-household allocation of resources
• Improving market specification in rural sector
– segmentation and market failures in factor markets
(land, labor, credit),
– spatial and regional dimensions in markets for goods
(access to markets, transaction costs)
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Poverty Analysis: The Road Ahead
• Microsimulation model of producers (farms,
firms) as well as households.
• Issue: use more representative actors
without moving to specification of all
observations in survey samples.
– Techniques of “data reduction” without loss of
important information.
• In MDG/MAMS: better models of links
between govt policy and MDG outcomes
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Poverty Analysis: The Road Ahead
• No single approach is likely to dominate
– Informational demands and operational
constraints vary across applications
• Data: reconciling household/firm data with
national accounts and SAM data
– Important for any poverty analysis.
– Separation of economywide and household
analysis represents a methodological failure
• Need for reconciliation and integration
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