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 such as Gravity Trade

Flow Models.

2 IFPRI Models

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 PROJECTSiwa Msangi + team…

Global Futures Launch Meeting1 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 Key linkages with exogenous ‘drivers’ of

change given by biophysical models – in particular, climate change

Global Futures enhancements Conclusions

The IMPACT Model

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 Wheat, Rice, Maize, Other Coarse Grains + Millet, Sorghum

Roots & Tubers Potatoes, Sweet Potatoes & Yams, Cassava & Other Roots and Tubers

Dryland legumes Chickpea, Pigeonpea, Groundnut

Livestock products Beef, Pork, Sheep & Goat, Poultry, Eggs, Milk

Fish Eight capture and aquaculture fish commodities plus fish meals and

fish oils High-Value

Vegetables, (Sub)-Tropical Fruits, Temperate Fruits, Sugar Cane, Sugar Beets and Sweeteners

Other 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

Page 12

Key linkages in modeling drivers & outcomes

child

malnutrition

Trade Equilibrium Balance

Rural Roads

Feed

Food

Price

Female educationSupply

Demand

Ag R&D investments

Domestic Biofuel ProdnOther

Demand

Policy drivers

Yield

Socioeconomic

Drivers

Climate change

Irrigation investments

Agric.

Imports/

exports

Area

Calorie Availability

Clean water access

Environmental driver

[investments]

Trade

policy

IMPACT Equations: Production

QS = quantity produced A = crop area, irrigated and rainfed Y = crop yield, irrigated and rainfedt = time indexn = country/FPU indexi = commodity indices specific for

crops

tni tni tniQS A Y

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

A = crop areaα = crop area interceptPS = effective producer price ε = area price elasticity

WAT = water stress = exogenous area growth rate (can

bealtered to reflect

urbanization, climate change, etc.

( ) ( ) (1 ) ;ijniintni tni tni tnj tni tni tni

j iA PS PS ga A WAT

tniga

IMPACT Equations: Yield Response, at FPU Level

Y = crop yield β = crop yield interceptPS = effective producer price = yield price elasticityk = inputs such as labor and

capitalPF = price of factor or input k = exogenous yield growth rateWAT = water stress

( ) ( )

(1 ) ( );

iin ikntni tni tni tnk

k

tni tni tni tni

Y PS PF

gy Y Y WAT

tnigy

IMPACT Food Demand, at Country Level Food demand is a function of commodity

prices, income, and population

Income (gI) and population (gP) growth rates exogenous

;)()()( tntntnjij

tnitnitni POPINCPDPDQF inijniin

1, (1 );tn t ni tnINC INC gI

);1(,1 tnnittn gPPOPPOP

Use CGE modeling to endogenize

IMPACT Feed Demand

Feed demand is a function of livestock production, feed prices, and feeding efficiency

( ) ( )

( ) (1 )

bn

bon

tnb tnb tnl tnbl tnbl

tno tnbo b

QL QS FR PI

PI FE

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

1,1, 1,

( )

( )tni tni

tni t nit ni t ni

QF QLQE QE

QF QL

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

tni tni tni tniQD QF QL QE

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 (1 )](1 )[ tnitnitni i = PSE ;PWPS MI

;CSE MI + PW = PD tnitniitni )1()]1([

;CSE MI + PW = PI tnitniitni )1()]5.01([

Consumer Prices

Feed Prices

Producer Prices

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

tni tni tni = - QT QS QD

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 market-clearing condition

;0 tni

n

QT

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 = Percent of malnourished children

PCKCAL = Per capita calorie consumption

SCH = Total female enrollment in secondary education as a % of the female age-group

LFEXPRAT = Ratio of female to male life exp. at birth

WATER = Percent of people with access to clean water

NMAL = Number of malnourished children, and

POP5 = 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)

• 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

The Bread & Butter of IMPACT

• 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 socio-economic conditions ( access to clean water, girls secondary schooling, rural roads ) on child malnutrition

Typical IMPACT-driven scenarios

• 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…

Issues that IMPACT cannot cover

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:

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 year2 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 functionLinear 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:

ttt IKK )1(1

• 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.