Models and Impact Evaluation Pasquale Lucio Scandizzo University of Rome Tor Vergata and The World Bank The three problems of impact evaluation Heckman 2010 P1 Evaluating ID: 626748
Download Presentation The PPT/PDF document "Computable General Equilibrium" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Computable General Equilibrium Models and Impact Evaluation
Pasquale Lucio Scandizzo
University
of Rome «Tor Vergata
» and The World
BankSlide2
The three problems
of impact
evaluation (Heckman, 2010)
P1. Evaluating
the impact of historical interventions on outcomes including their
impact
in
terms
of welfare
.
P2. Forecasting
the impacts
(
constructing counterfactual states
)
of interventions
implemented in
one environment in other environments, including their impacts in
terms
of
welfare
.
P3. Forecasting
the impacts of interventions
(
constructing counterfactual states
associated with
interventions
)
never historically experienced to various
environments,including
their impacts in terms of welfare
.Slide3
The general framework
Assume a
well defined set of individuals ω ∈ Ω and a universe of counterfactuals or hypotheticals for each
agent
Y(
s,ω
)
,
s
∈
S
. Different policies
p
∈
P
give different incentives by assignment
mechanism
a
to agents who are allocated to treatment by a rule
τ
∈
T
.
No
well defined rules for constructing counterfactual or
hypothetical states
or constructing the assignment to treatment
rules.
Economic theories
provide algorithms
for generating the universe of internally consistent, theory-consistent
counterfactual
states
.Slide4
The Policy Invariance Goal
According to
Marschak’s Maxim, the goal of explicitly
formulated and quantified economic models
is to
identify
policy-invariant
or
intervention-invariant
parameters that can be used to
answer classes
of policy evaluation
questions.
Policy
invariant economic parameters may or may not
be interpretable
economic parameters
.
The
treatment-effect literature also seeks to
identify intervention-invariant
parameters for a class of interventions. In this sense the
structural and
treatment effect literatures share common
objectives. Slide5
CGE as a Causal Model
Three distinct tasks arising in the analysis of causal models
Defining the set of hypotheticals or counterfactuals A scientific theory
Identifying
parameters (causal or otherwise)
from
hypothetical
population
data :
Mathematical
analysis of point
or
set
identification
Identifying parameters from real data Estimation and testing theorySlide6
Definitions of Counterfactuals
• Identification of causal models from idealized data of population distributions (
infinite samples without any sampling variation). The hypothetical populations may be
subject to selection bias, attrition and the like. However, all issues of
sampling variability
are irrelevant for this problem.
• Identification of causal models from actual data, where sampling variability
is an
issue. This analysis recognizes the difference between empirical
distributions based
on sampled data and population distributions generating the data.Slide7
A generalized Roy Model (1)
Suppose that there are
S states associated with different levels outcome
such as
production, consumption,
or choice of technology.
witheach
choice
s
is a valuation of the outcome of the choice
R(s)
, where
R
is the
valuation function
and
s
is the state.
Define
Z
as individual variables that affect choices.
Each state
may be characterized by a bundle of attributes, characteristics or qualities
Q(s)
that
fully characterize the state. If
Q(s)
fully describes the state,
R(s)
=
R(Q(s))
. A
ssume that the
Z
is
observed and that additive
separability
is applicable.
Let
ν
denote unobserved components as perceived by the econometrician:
R(s
)
=
μ
R(
s,Z
)
+
η(
s,Z
,
ν
),
where
μR
(
s,Z
)
is the deterministic component of the utility function expressed in
terms of
observed variables
Z
and
η(s, Z, ν)
represents
unobservables
from the point of
view of
the econometrician
.Slide8
A generalized Roy Model (2)
Associated with each choice is outcome
Y(s) which may be vector valued
.
These outcomes
can depend on
X
. The outcome model is thus:
Y(s)
=
μY (s,X)
+
U(s,X, ε
)
The set of possible treatments
S
is {1
, . . . ,
￣
S
}, the set of state labels. The set of
counterfactual
outcomes
is
{
Y(
s,X
)
}
s
∈
S
. The treatment assignment mechanism is
produced
by
utility
maximization
:
D(j )
= 1 if
argmax
R(s)
=
j
s
∈
S
Thus
agents
self
select
into treatment (other selection rules can also be specified)
and the probabilities of selection which are defined at the
individual level
are either zero or one for each agent (agents choose outcomes with certainty
).
Policies
can operate
to
change
Z,X
, and the distributions
η(s, Z, ν)
,
U(
s,X
, ε)
.Slide9
The general equilibrium economic
model
as a policy variant parameter
system
Figure 1 : The Basic Economic Model
Productive Capacity
Product Prices
Factor Prices
Employment
Production
Consumption
IncomeSlide10
Prod.Capacities produttiva
Consumi
Production
Employment
Factor Demand
Incomes
Factor Prices
Factor Supply
Final Demand
Product Supply
Capital Stock Changes
Product PricesSlide11
A generalized SAM –CGE Model (Primary Equations
under Policy
Invariant Parameters)Slide12
A generalized SAM –CGE Model (Dual Equations
under Policy
Invariant Parameters)Slide13
A differential Version (Policy Variant
Parameters
)
(9) Slide14
Endogenous and exogenous components
(11)
(12) Slide15
Policy transaction matrix
definition
(Policy Variant Parameters)Slide16
CGE Implicit SolutionSlide17
From the transaction definition it
follws that.
∆
is
a
divergence
matrixSlide18
At
least
one
exogenous
sector
because
of
Walras
LawSlide19
CGE
as
a generalized SAMSlide20
Explicit Solution with parameter change
rulesSlide21
CGE Explicit Solution
Generalized
supply
function
Explicit
solution
with
parameter
variations
rulesSlide22
An Example: The Impact of Investment in the Ocean Economy in MauritiusSlide23
Key messagesAn investment strategy based on boosting the ocean economy beyond its traditional boundary appears to be a smart choice for Mauritius to achieve balanced and sustained growth over the next ten years.
With a cumulative investment of $5.8 billion over ten years, the ocean economy would almost double by the end of the simulation period, account for 20% of GDP, and be 20% more diversified
Investing those funds in Ocean economy would be better than in a plausible alternative scenario: it would generate an additional 20% payoff on investment, generate 36% more jobs, make the poor better off, and reduce (slightly) the debt/ GDP ratio
To achieve this potential it is essential to create attractive conditions for private sector investment, invest in human capital, and conserve environmental qualitySlide24
Approach: O2 and CF scenarios
The economic model used in the simulations is based on the statistics provided by Statistics Mauritius(SM) and on a Social Accounting Matrix developed in collaboration with specialists from SM and the Ministry of Ocean Economy.
The model was calibrated to reproduce Mauritius historical experience and fits well the past production and consumption time series.
The model was used to simulate two alternative scenarios: one based on the investment on the Ocean Economy (named ) O2, and one with the same historical structure of investment, representing a viable alternative (named counterfactual or CF).Slide25
The model fits well Mauritius past
growthSlide26
The treatment effect: OE2 Scenario outperforms the
Counterfactual
(CF)
The OE2 scenario appears to outperform the CF in all the macro indicators considered, with differences tending to increase over time.
The average contribution to growth of the OE2 scenario is significantly higher (3.17 percent) than the CF scenario (2.93 percent).
The cumulative return, as measured by the ratio of the present value of additional GDP and investment is more than twice as much in OE2 (49 percent) versus CF (23 percent).
Its effect on factor incomes (value added) and job creation is also larger than the CF scenario However, the OE2 scenario shows an increase in the capital income component of GDP, and its environmental costs (and the implicit investment costs to neutralize them) are much higher for OE2 than for CF.
However, because of its reliance on ocean resources, even though its pressure on the small land basis of Mauritius is low, the OE2 strategy is likely to result in sizable environmental costs. Slide27
Treatment effect: Doubling the Ocean Economy Offers More Job Creation (Job creation in comparison with the counterfactual (CF)
Number of Jobs created
OE2
CF
Labor Qualification
Year 1- 10
Year 1- 10
Primary Education
2388
3032
Secondary Education <SC
2936
1649
Secondary Education SC and above
1215
981
Tertiary Education
2694
2271
Own Account
3492
1390
Total
12,726
9,324Slide28
Policy Invariance? No: OE2 would
change
sector structure and
response
parameters
Sector Production
as
% of OE Production
Year 1
Year 10
Fishery and Sea food proc
18%
22%
Sea Transport and Related Services
10%
13%
Marine ICT
7%
8%
Tourism
64%
55%
Sewage and Water Treatment
2%
2%
Total
100%
100%Slide29
Probabilitic Impact: The OE2 scenario creates more
value
and improves the income distribution
OE2 investment-CF scenario, percent increases in value-added components
Income distribution effects of OE2, percent OE2 changes versus CF baselineSlide30
Impact of Ocean Economy
appears robust under
stress (Performance of the counterfactual scenario =100)
Performance Metric
Base case (no constraints)
A. Unfavorable international finance
B: A plus constrained skilled labor supply
C: B plus natural resource constraints
Average Contribution to Growth
108.19
190.60
107.27
95.26
NPV (5%)GDP GROWTH
127.66
121.58
122.06
103.75
NPV GDP/NPV INV
121.14
121.10
121.69
104.13
) Slide31
..but key constraints must be overcome
Boost productivity. I
n order to achieve a desirable 5% rate of growth, the country will need to significantly increase its average rate of total factor productivity growth, by about 1.8 percent under the OE2 strategy, which is a relatively large amount, even though less than the 2.7 percent under the CF strategy. Strengthen the fiscal stance.
Some fiscal consolidation, with somewhat higher saving rates, lower government expenditure, and higher reliance on private domestic investment, appears to be necessary to secure a firmer base for growth.
Invest in education and training.
While OE2 promises a high degree of job creation, its reliance on new and technologically more sophisticated sectors requires focusing on improving the school system and reforming vocational education. The private sector can help by facilitating re-training and special skill transfers through privately financed programs.Slide32
but key constraints must be overcomeConserve and improve natural resources.
Even though the OE2 strategy lessens some of the pressure on land-based activities that rely on natural resources, the simulations show that its end use of ocean and internal waters turns out to be much more intensive than the counterfactual.
Moreover, much of the country’s natural resources are now being exploited at no charge, maintenance and renewal activities are low, and pollution and other forms of degradation appear to be rampant.
Thus, investments in ocean environmental goods are essential. This means replacing the current model of rent exploitation with significant investment in the conservation and improvement of natural resources. Possible solutions include marine spatial planning and lagoon rehabilitation, improved sanitation and water treatment, and scaling up appropriate environmental regulations. Slide33
The way forward
Mauritius could update and extend the CGE model to evaluate different scenarios as the OE strategy and other government policies are deployed.
Mauritius’understanding of the relationships between key parameters, e.g. between investment and income distribution, could be improved under the feedback provided by new data and analyses of impact and cost benefit of specific projects.
The model can also be used for training to further empower the government’s statisticians and economists, who have already produced an exemplary set of methodologies, national statistics, and economic accounts.
The model could be used in the nest stages of the implementation of the Ocean Development strategy, by providing a consistent framework to define GDP and employment creation targets, to analyze trade-offs in allocating investment resources between Ocean/ Non Ocean sectors; and among Ocean sectors.Slide34