Transcript Empirical approaches to trade modeling

EMPIRICAL APPROACHES TO TRADE
MODELING-CGE AND PARTIAL
EQUILBRIUM
LECTURE 12: AHEED COURSE “INTERNATIONAL AGRICULTURAL
TRADE AND POLICY”
TAUGHT BY ALEX F. MCCALLA, PROFESSOR EMERITUS, UC-DAVIS
APRIL 6, 2010 UNIVERSITY OF TIRANA, ALBANIA
Lecture drawn from IFPRI materials
Approaches to Trade Modeling

There are basically three widely used techniques of
modeling trade:
 Computable
General Equilibrium Models (CGEs);
 Partial Equilibrium Models (PEMs) frequently of two
sub-types;
 Spatial
equilibrium models which model physical distances;
 Non-spatial models which link countries with transport cost
functions (PEM-NS)
 Econometric
Models.
Models such as Gravity Trade Flow
2 IFPRI Models

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

We will look at two types of models used most
frequently in trade analysis-CGEs and PEM-NS:
The first ids the IFPRI IMPACT Model, a PEM-NS,
where I will share slides provided by Siwa Msangi
of IFPRI;
The second is the IFPRI MIRAGE CGE model. I will
share slides provided byAntoine Bouet of IFPRI.
Thanks to both of them and IFPRI
THE IMPACT MODEL AND
PLANNED IMPROVEMENTS IN
THE GLOBAL FUTURES PROJECT
Siwa Msangi
+ team…
Global Futures Launch Meeting
1 March 2010, IFPRI, Washington, D.C.
Overview

Introduction to the IMPACT Model
Coverage (spatial, commodity)
 Basic equations (“the guts”)
 Linkages to well-being outcomes (esp. nutrition)
 Key data (what goes in) + outputs (what comes out)
 Typical applications – what it does and does not do

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Key linkages with exogenous ‘drivers’ of change given
by biophysical models – in particular, climate change
Global Futures enhancements
Conclusions
The IMPACT Model

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
IMPACT – “International Model for Policy Analysis
of Agricultural Commodities and Trade”
Representation of a global competitive agricultural
market for crops and livestock
Global
 115
countries
 281 food production units
 32 agricultural commodities
32 IMPACT Commodities

Cereals


Roots & Tubers


Vegetables, (Sub)-Tropical Fruits, Temperate Fruits, Sugar Cane, Sugar Beets and
Sweeteners
Other


Eight capture and aquaculture fish commodities plus fish meals and fish oils
High-Value


Beef, Pork, Sheep & Goat, Poultry, Eggs, Milk
Fish


Chickpea, Pigeonpea, Groundnut
Livestock products


Potatoes, Sweet Potatoes & Yams, Cassava & Other Roots and Tubers
Dryland legumes
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Wheat, Rice, Maize, Other Coarse Grains + Millet, Sorghum
Soybeans, Meals, Oils
Non-food

Cotton, Biofuel products (ethanol, biodiesel)
Global Economic Regions (115)
Global Basins (126)
Global Food Production Units
(281 FPUs)
Higher river basin spatial resolution planned for better water availability modeling
IMPACT Basics
Global, partial-equilibrium, multi-commodity
agricultural sector model
 Links country-level supply and demand through
global market interaction and prices
 Country-level markets are linked to the rest of the
world through trade
 World food prices are determined annually at
levels that clear international commodity markets

Key linkages in modeling drivers &
outcomes
Policy
drivers
Other
Demand
Demand
Agric.
Trade
Imports/
policy
Trade
Equilibrium
Balance
Domestic
Biofuel Prodn
Feed
Socioeconomic
Food
Drivers
child
Price
Calorie
Availability
exports
Area
Supply
Yield
Climate
change
Irrigation
investments
Rural
Roads
malnutrition
Clean water
access
[investments]
Ag R&D
investments
Environmental driver
Page 12
Female
education
IMPACT Equations: Production
QStni  Atni  Ytni
QS
A
Y
t
n
i
= quantity produced
= crop area, irrigated and rainfed
= crop yield, irrigated and rainfed
= time index
= country/FPU index
= commodity indices specific for crops
IMPACT Area and Yield Functions



Area – function of crop prices and other sources of
growth (exogenous and others modeled)
Yield – function of crop and input price, and other
sources of growth
Underlying yield growth are implicit policy drivers
that are not directly embedded in the simulation
 Public
and private research
 Markets, infrastructure, irrigation investments
IMPACT Equations: Area Response, at
FPU Level
 iin
Atni  tni  ( PStni )
 ( PStnj )  (1  gatni )  Atni WATtni  ;
 ijn
j i
A
α
PS
ε
=
=
=
=
crop area
crop area intercept
effective producer price
area price elasticity
WAT
=
water stress
gatni
=
exogenous area growth rate (can be
altered to reflect urbanization, climate
change, etc.
IMPACT Equations: Yield Response, at
FPU Level
Ytni  tni  ( PStni )
g iin
 ( PFtnk )
g ikn
k
(1  gytniYtni )  Ytni (WATtni );
Y
β
PS
g
k
PF =
gytni
WAT=
=
=
=
=
crop yield
crop yield intercept
effective producer price
yield price elasticity
=
inputs such as labor and capital
price of factor or input k
=
exogenous yield growth rate
water stress
IMPACT Food Demand, at Country
Level
Food demand is a function of commodity prices,
income, and population
 ijn
 iin
QFtni  tni  ( PDtni )   ( PDtnj )  ( INCtn )in  POPtn ;

j i

Income (gI) and population (gP) growth rates
exogenous
INCtn  INCt 1,ni  (1  gItn ); Use CGE modeling to endogenize
POPtn  POPt 1, ni  (1  gPtn );
IMPACT Feed Demand

Feed demand is a function of livestock production,
feed prices, and feeding efficiency
QLtnb  tnb  (QStnl  FRtnbl )  ( PI tnb )g bn
l
  ( PI tno )g bon  (1  FEtnb )
o b
l
= commodity indices specific for livestock commodities
b
= commodity indices specific for feed commodities
IMPACT Other Demand

Other demand grows in the same proportion as food
and feed demand
(QFtni  QLtni )
QEtni  QEt 1,ni 
(QFt 1,ni  QLt 1,ni )
 In the case of biofuel – this other category represents the
feedstock demand for particular commodities
IMPACT Total Demand

Total demand is the sum of food, feed, and other
demand
QDtni  QFtni  QLtni  QEtni
IMPACT Price Determination

Prices are endogenous
Domestic prices – function of world prices, adjusted by
effect of price policies, expressed as producer subsidy
equivalents (PSE), consumer subsidy equivalents (CSE), and
the marketing margin (MI).
 MI currently single value per country. Will make spatial

Producer Prices
PS tni = [ PW i (1  MI tni)](1  PSEtni);
Consumer Prices
PDtni = [ PW i (1+ MI tni)] (1  CSEtni);
Feed Prices
PI tni = [ PW i (1+ 0.5 MI tni)] (1  CSEtni);
IMPACT Net Trade

Commodity trade is the difference between domestic
production and demand. Countries with
 positive
trade are net exporters
 negative values are net importers
QT tni = QS tni - QDtni
For some commodities, stock change would be included in this equation – the
methodology is currently under revision
IMPACT Market Clearing Condition

Minimize the sum of net trade with a world market
price for each commodity that satisfies the marketclearing condition
 QT
n
tni
 0;
Number and Percentage
Malnourished Children
Malnourished children are projected as follows:
%ΔMALt= - 25.24 * Δt-1 ln (PCKCAL) - 71.76 Δt-1 LFEXPRAT
- 0.22 Δ t-1SCH - 0.08 Δt-1 WATER
NMALt = %MALt x POP5t
%MAL
PCKCAL
SCH
LFEXPRAT
WATER
NMAL
POP5
= Percent of malnourished children
= Per capita calorie consumption
= Total female enrollment in secondary
education as a % of the female age-group
= Ratio of female to male life exp. at birth
= Percent of people with access to clean water
= Number of malnourished children, and
= Number of children 0 to 5 years old
IMPACT Starting Values


2000 FAOSTAT. Will update to 2005
ISPAM 5 minute production and area data (also tuned to
2000 FAOSTAT). Will update to 2005





HarvestChoice product
Plausible allocation of 20 crops (soon to be 30) spatially based on
agroclimatic conditions and known regional production statistics
Hydrology uses Univ. of East Anglia data, and streamflow is
calibrated to WaterGAP model results
Prices based on World Bank ‘pink sheets’ and other sources
Elasticity values taken from previous IMPACT values, and
adjusted for the purposes of calibration in some cases
IMPACT Outputs








Supply
Demand (food, feed, and other demand)
Net trade
World prices
Per capita demand
Number and percent of malnourished children
Calorie consumption per capita
Plus

Water use, (at some point: soil carbon, total biomass)
The Bread & Butter of IMPACT
• Much of the past work of IMPACT has centered around
providing a forward-looking perspective on what’s
needed to meet future food needs, and the implications
for key CGIAR mandate commodities
• Because it was designed to look at the long term, that
aren’t covered by others (USDA, FAPRI, OECD), the results
are better used for projections and not prediction –
which implies that you’re more interested in deviations
from a baseline, under alternative scenarios, rather than
point estimates
• Can be useful for determining which crop improvements
have the biggest effect on food availability and levels of
malnutrition
Typical IMPACT-driven scenarios
• Looking at the implications of socio-economic
growth (income, population) on food/feed
demand and other indicators mentioned above
• Looking at the implications of higher factor prices
(fertilizer, labor) on crop yield – and production
• Fairly simple trade liberalization or protection
scenarios (with phased changes over time)
• Looking at implications of improved socioeconomic conditions ( access to clean water, girls
secondary schooling, rural roads ) on child
malnutrition
Issues that IMPACT cannot cover
• Explicit projections on poverty or household-level
income changes
• Modeling the endogenous feedbacks between input
prices and agricultural output and price changes
• Going directly from agricultural gross production value
(revenue) to total agricultural value-added
• Going from changes in implied changes in child
malnutrition levels to changes in number of total
malnourished in the population (except by assumption,
perhaps….)
• Other implications for non-agricultural sectors…
Applications of IMPACT


The IMPACT model is used most often for long run
projections but also can be used for trade policy
analysis.
Chapter 4 in McCalla & Nash by Mark Rosegrant &
Siet Meijer presents the results of four trade
liberalization scenarios:
 In
terms of impacts on cereal and livestock trade;
 Impacts on commodity prices;
 Economic benefits of trade liberalization.
TRAINING SESSIONS ON THE MIRAGE MODEL AND ON THE
MACMAP-HS6 DATABASE
THE MIRAGE MODEL – STRUCTURE AND THEORY
Antoine Bouet
David Laborde
Marcelle Thomas
Rabat, Mars 2010
A. Presentation of the MIRAGE
model

Data sources = inputs for the model

Main hypotheses of the model
A. Presentation of the MIRAGE
model
MIRAGE = Modeling International Relationships in Applied General
Equilibrium
Brief reminder:
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





CGEM devoted to trade policies analysis
Multi-country
Multi-sector
5 primary factors
Perfect & Imperfect competition
Horizontal (variety) & Vertical (quality) differentiation
Static vs. Dynamic (sequential)
Data sources
The calibration of the MIRAGE model is computed
from data for a base year
2 main data sources:

 GTAP
v. 6.1 database (2001) or GTAP v. 7 database
(2004)
 MAcMap-HS6 database (2004)
Data sources
GTAP = Global Trade Analysis Project



Purdue University (USA, Indiana)
Data on world trade (bilateral flows,…), production, consumption,
intermediate use of commodities and services
Disaggregation (GTAP 7) covering (57 sectors and 113 regions)

New regions added to version 7 include: Armenia, Azerbaijan, Belarus, Bolivia, Cambodia,
Costa Rica, Ecuador, Egypt, Ethiopia, Georgia, Guatemala, Iran, Kazakhstan, Kyrgyzstan, Laos,
Mauritius, Myanmar, Nicaragua, Nigeria, Norway, Pakistan, Panama, Paraguay, Senegal and
Ukraine
( https://www.gtap.agecon.purdue.edu/ )
 Interest: use this Global database as a Global Social Accounting Matrix for
the MIRAGE model
Data sources
MAcMap-HS6 = Market Access Maps

ITC (UNCTAD-WTO) and CEPII

Data on market access (bilateral applied tariff duties - taking into account
regional agreements and trade preferences; information given at the HS6
level)
Data come from: national sources and IDB (Integrated DataBase) from the
WTO
(http://www.ifpri.org/book-5078/ourwork/program/macmap-hs6)


Interest: replace tariffs coming from GTAP database by the ones coming
from MAcMap-HS6 into the MIRAGE model
A. Presentation of the MIRAGE
model
Main hypotheses of the model
General Structure




MIRAGE = Modeling International Relationships in Applied
General Equilibrium
r,s regions
i,j Goods
Input/Output tables and bilateral trade




I*R*S and I*J*R: large number of flows
One representative agent per region
Five factors
Firms per sector:


One in perfect competition
N homogenous in imperfect competition
Main hypotheses of the model

Production factors

Skilled labor: perfect mobility between sectors

Unskilled labor: imperfect mobility between agricultural and non agricultural
sectors - perfect mobility amongst each group’s sectors ; another
specification is possible: Lewis model in some Dvg countries

Land: imperfectly mobile between sectors

Natural resources: sector-specific and constant

Capital: sector-specific and accumulative
Demand

Three types of demand:





final consumption: LES-CES function
intermediate consumption: CES
capital good (from fixed saving rate on revenues): CES
Supplied by domestic production or imports
Several levels of differentiation:



Quality (2 geographical zones)
Domestic vs. imports if in same quality zone
Differentiation by regions within each quality zone
Main hypotheses of the model
Final Consumption: LES-CES function
Linear Expenditure System - Constant Elasticity of Substitution

The demand structure of each region depends on its income level (i.e.: a minimum
level of the final consumption is assumed for each region according to the income
level of which one the consumer is issued)

In MIRAGE, minimum levels of consumption:
 1/3 for developed countries
 2/3 for developing countries
All others characteristics as a CES function


New version of the MIRAGE model: new calibration procedure of the CES – LES in
order to generate price and income elasticities which are compatible (by sectors and
regions) with those estimated by USDA-ERS.
Main hypotheses of the model

Product differentiation (3 levels by nested Armington)
Armington hypothesis: choice between products based on geographical origins
(differentiations by geographical origins)

2nd level : 2 quality ranges from geographical basis → 2 zones
 Zone U = regions from the same quality of the region of the buyer
 Zone V = regions from the other quality
In MIRAGE, goods produced by developed countries are assumed to have a
different quality than the ones produced by developing countries)
Main hypotheses of the model


3rd level: same hypothesis inside a same Zone of quality (vertical
differentiation: local goods are assumed to be different than foreign ones
 Local region
 Foreign regions
4th level: same hypothesis inside foreign regions: goods produced inside
each foreign region are assumed to be different than the one produced
each other one
 Foreign region 1
 …
 Foreign region n
 …
 Foreign region N
Main hypotheses of the model
Product differentiation: horizontal differentiation
(in case of imperfect competition only)

 5th level:
Dixit-Stiglitz differentiation which implied a
difference in variety among the goods
 Characteristics:
same as a CES function with 1 innovation
→ Allow the number of arguments to be variable
Main hypotheses of the model

Imperfect competition characteristics

Cournot-Nash oligopolistic competition

Number of firms = number of varieties

Increasing returns to scale modeled by a fixed cost in terms of output (calibrated for profits=0
in the base year)

In the short term, positive mark-up depending on demand elasticity
In the long term adjustment of the number of firms such that profits go down to 0


Number of firms/varieties, substitution elasticities and markups calibrated jointly in order to
minimize an objective given estimated values that are not fully consistent with each other
Specifications of factor markets

Segmentation of unskilled labour market

Developed countries


CET: Segmentation between urban sectors and rural sectors
Developing countries

Can be changed considering unrestricted labour reserve in rural areas of
high populated developing countries



Real wage perfectly flexible such that supply = demand
New migrants to cities allows for infinitely elastic labour supply in industry
Land supply: two levels of extension


Scarce land or not scarce land
More complex nesting trees are possible (see our biofuels studies)
Main hypotheses of the model

Modeling of tax and trade obstacles in MIRAGE

Production tax (modeled as an ad-valorem tax)

Export tax

Quotas (modeled as an export tax)

Trade restrictions on goods and services (modeled as an ad-valorem
equivalent)

Indirect taxes on the three types of demand (final, intermediate and capital)
Main hypotheses of the model

Modeling of transport of goods in MIRAGE

Produced like any other kind of service

Transport demand is proportional to the volume of goods transported

The proportionality coefficient varies with:
 The type of good
 The location of the production
 The destination
(coefficient defined on i=sector, r=supply ,s=demand)
Main hypotheses of the model

Modeling of Capital and Investment in MIRAGE

Installed Capital is sector-specific and immobile: the rate of return to
capital may vary across sectors and regions

Investment (domestic and foreign) is the only adjustment variable for
capital stocks such as:
Kt  Kt 1 (1   )  I t
• Possibility of transnational investment with use of external datasets
Investment allocation



•
Portfolio allocation strategy
Substitution between the different assets is not
perfect (risk aversion)
A single formulation is used for setting both
domestic and foreign investment:
where r stands for the return rate of capital
A depends on market size
Financial closure

Exogenous & constant saving rate to finance



local investment
foreign direct investment (FDI)
Exogenous external credit to the regional agent
Current account
= -(FDI in – FDI out + Accumulation or loss of
monetary assets – exogenous-)
•
Regional income sources:
- factor returns
- tax returns
- net FDI returns
- exogenous financial flows
•
Regional expenses:
- final consumption
- local investment and FDI
Dynamics


Recursive dynamic: no rational expectations
Dynamics driven by exogenous changes in





Labour stock
TFP evolution (more precisely TFP evolution reflects exogenous GDP
predictions)
Capital accumulation endogenously computed through
depreciation and accumulation
Land accumulation endogenously computed through a relation
between land supply and real return of land
Regional TFPs endogenously computed in the baseline to match
regional growth targets
A Flexible Tool





Aggregation can be changed
Some options can be reset (quality zone, land closure,
etc.)
Static and dynamic mode
Perfect and imperfect competition
Different designs of labour markets
CGE MODEL USE



CGE models have been used extensively to look at who
would gain or lose from trade liberalization.
The World Bank has used the GTAP model (a CGE) to
analyze global impacts. See Chapter 6 in McCalla &
Nash by Dimaranan, Hertel and Martin for an example
of its use.
In the same volume Chapter 2 by Huff, Krivonos ans van
der Mensbrugghe compares these two approaches with
several other modeling efforts.