Lecture drawn from IFPRI materials

1. Empirical approaches to trade modeling-cge and partial equilbriumLecture 12: aheed course “international agricultural trade and policy”Taught by alex f. mccalla, professor emeritus, uc-davisApril 6, 2010 university of tirana, albania Lecture drawn from IFPRI materials

2. 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.

3. 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 SiwaMsangi of IFPRI; • The second is the IFPRI MIRAGE CGE model. I will share slides provided byAntoineBouet of IFPRI. • Thanks to both of them and IFPRI

4. The IMPACT Model and Planned Improvements in the Global Futures Project Global Futures Launch Meeting 1 March 2010, IFPRI, Washington, D.C. Siwa Msangi + team…

5. 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

6. 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

7. 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)

8. Global Economic Regions (115)

9. Global Basins (126)

10. Global Food Production Units (281 FPUs) Higher river basin spatial resolution planned for better water availability modeling

11. 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

12. Policy drivers Domestic Biofuel Prodn Other Demand Demand Socioeconomic Drivers Feed Food Agric. Imports/ exports Trade Equilibrium Balance Trade policy child malnutrition Price Calorie Availability Irrigation investments Area Female education Clean water access Supply Rural Roads Yield [investments] Ag R&D investments Climate change Environmental driver Key linkages in modeling drivers & outcomes Page 12

13. IMPACT Equations: Production QS = quantity produced A = crop area, irrigated and rainfed Y = crop yield, irrigated and rainfed t = time index n = country/FPU index i = commodity indices specific for crops

14. 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

15. IMPACT Equations: Area Response, at FPU Level A = crop area α = crop area intercept PS = effective producer price ε = area price elasticity WAT = water stress = exogenous area growth rate (can be altered to reflect urbanization, climate change, etc.

16. IMPACT Equations: Yield Response, at FPU Level Y = crop yield β = crop yield intercept PS = effective producer price g= yield price elasticity k = inputs such as labor and capital PF = price of factor or input k = exogenous yield growth rate WAT = water stress

17. 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 Use CGE modeling to endogenize

18. IMPACT Feed Demand • Feed demand is a function of livestock production, feed prices, and feeding efficiency • l = commodity indices specific for livestock commodities • b = commodity indices specific for feed commodities

19. IMPACT Other Demand • Other demand grows in the same proportion as food and feed demand • In the case of biofuel – this other category represents the feedstock demand for particular commodities

20. IMPACT Total Demand • Total demand is the sum of food, feed, and other demand

21. 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 Consumer Prices Feed Prices

22. 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 For some commodities, stock change would be included in this equation – the methodology is currently under revision

23. IMPACT Market Clearing Condition • Minimize the sum of net trade with a world market price for each commodity that satisfies the market-clearing condition

24. 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 Δt-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

25. 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

26. 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)

27. 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

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

29. 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…

30. 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.

31. 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

32. Presentation of the MIRAGE model • Data sources = inputs for the model • Main hypotheses of the model

33. 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)

34. 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)

35. 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

36. 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

37. Presentation of the MIRAGE model Main hypotheses of the model

38. General Structure • MIRAGE = Modeling International Relationships in Applied General Equilibrium • r,sregions • i,jGoods • Input/Output tables and bilateraltrade • I*R*S and I*J*R: large number of flows • One representative agent per region • Five factors • Firms per sector: • One in perfectcompetition • N homogenous in imperfectcompetition

39. 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

40. 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

41. 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.

42. 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)

43. 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

44. 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

45. 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

46. 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)

47. 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)

48. 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)

49. 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: • Possibility of transnational investment with use of external datasets

50. 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