@2030 modelling system – major enhancements

Major enhancements of @2030 modelling system

Wolfgang Britz, May 29, 2003

@2030 in a nutshell

@2030 is a non-spatial, recursive-dynamic multi-commodity model for agricultural products. It is solved on a yearly basis. Its parameterization is primarily based on existing FAO models, notably the World Food Model (WFM, version 1995) and the Food Demand model (FDM). Where these models fail to provide parameters, external sources and/or “guestimates”[1] have been used to close the remaining gaps. Behavioural parameters have been calibrated to comply with micro-economic conditions and are adjusted dynamically over time to reflect appropriately the long-term dynamics in food and agriculture.

The model consists of identically structured regional modules describing supply and demand (food, feed and industrial use) for agricultural products where differences are expressed by regionally specific parameterization of equations. The systems of behavioural equations for supply and feed are based on normalized quadratic profit functions, are globally well-behaved and linked consistently with balances and endogenous prices for energy and protein requirements. Food consumption is determined through a Generalised Leontief expenditure system. The system allows to calculate changes in profits of farmers and welfare of consumers, whereas public budget effect are derived through and from changes in Producer Subsidy Equivalents (PSEs)[2].

Regional and world markets are cleared by adjusting uniform world market prices so that net trade is minimised[3]. Markets are treated as points, transport costs are excluded, all commodities are assumed to be homogenous.

Other important features include:

  • A focus on long-term developments, reflected in a number of features, most importantly in the fact that all elasticities are dynamic. Income elasticities for instance are a function of the income levels and decline as income levels rise.
  • The framework captures all food commodities, which allows to monitor changes in food consumption patterns, food consumption (calorie intake) levels and thus draw inferences on changes in food security.
  • A distinction is made between consumer and producer markets. Consumer prices for food and fibre are for instance distinguished from producer prices for the agricultural commodities. Processing and distribution margins represent a wedge between the two, depending primarily on the level of economic development [not yet fully implemented].
  • Agricultural policies of OECD countries and selected transition economies are represented by PSEs. Total PSEs are split into an element of market price support and a non-price related support element. This provides a possibility to gauge the impacts of a comprehensive liberalisation scenario that could be envisaged over the long run.
  • Agricultural policies for non-PSE countries are not yet comprehensively covered. The domestic-to-international price-wedge for developing countries distinguishes policy-related protection and “natural” protection[4].

Technically, the model is realized in GAMS, a widely used tool in quantitative economic analysis, and directly linked to HTML table and a Java based mapping tool.

Content

1.Background

2.Country and commodity coverage

3.Price system

3.1.Overview

3.2.Consumer prices

3.3.Endogenous prices for energy and protein in feed

3.4.Equations in the price framework

4.The quantity framework in @2030

4.1.Overview

4.2.Food use

4.2.1.Demand system

4.2.2.Income dependent demand elasticities

4.2.3.Calibration of demand elasticities

4.3.Partial adjustment process on the supply side

4.3.1.The supply system

4.3.2.Calibration of supply elasticities

4.4.Feed use

4.4.1.Feed demand system

4.4.2.Feed conversion factors for meat and animal products

4.5.Industrial processing

4.6.Welfare analysis

5.Technical documentation

5.1.Data update

5.2.Integration of new raw data (both expost and exante)

5.2.1.World price trends

5.2.2.Data aggregation to regions

5.2.3.Feed conversion factors

5.2.4.Hodrick-Prescott filer

5.3.Calibrating behavioural functions to a new data set

5.3.1.Regional aggregation of parameters from WFM

5.3.2.Parameter calibration

5.4.Scenario steering of the system

5.5.The base line

5.6.Defining counterfactual scenarios

5.6.1.Using the equation/variable listing for debugging

5.6.2.A direct look at the result array with a text editor

5.6.3.Using GAMSVIEW

5.6.4.The HTML tables

5.6.5.The mapping tool

6.Technical solution

7.References

The @2030 modelling system

The following description is thought both as technical guide to the model, and as document for a reader interested in the system. A basic understanding of common quantitative modelling approaches and micro-economic theory is assumed. Especially the last chapter is very technical and can be skipped if the main interest is on the structural and methodological part of the system.

1.Background

The FAO has recently prepared its fourth long-term outlook for world agriculture, called “World Agriculture: Towards 2015/30” (AT2030). The predecessors were the AT2000 study of 1979, the AT2000 study of 1987, and the AT2010 study of 1995. These studies are based on a variety of different techniques, ranging from formal modelling approaches to soliciting, quantifying and embedding expert knowledge. The results of these approaches are brought together into a large accounting scheme that aims to ensure consistency between the various inputs. While the process helps ensure consistency in the end results, it fails to retain the formal links that existed within the various models and across the different approaches. This makes it difficult to reproduce the baseline projections and impossible to gauge how a change in the underlying assumptions would alter the projected baseline outcome. In short, the lack of a fully formalized system makes it difficult to undertake and analyze alternative scenarios.

These drawbacks have been the starting point for the development of a new modelling system. The main purpose of this effort is to provide a provisional model-based complement to the current AT2030 exercise that allows for simple scenario analysis around the existing baseline. The experience gathered in creating and using this system should help build-up a more comprehensive modelling framework for long-term projections and scenario analyses[5]. After a first prototype version was developed and applied for first scenario analysis in 2001, this paper discusses the next step in the creation of such a system, one of which is meant to be a stepping-stone towards the final product. With the second step now being completed, the basic structure of the @2030 modelling system is outlined below, and the documentation is expanded to cover the first elements of a user guide.

2.Country and commodity coverage

The new version of the model distinguishes 36 countries or country groups, compared to the 17 found the first version. Table 1 summarizes the new regional groupings. It should be noted, however, that parameters and base data are maintained at the individual country level which provides for alternative aggregations. Both more aggregated and more disaggregated commodity or country groupings can readily be derived from the existing set of data and parameters.

The resulting expansion of the system certainly offers improved impact analysis at regional level. On the other hand, the higher level of disaggregation makes data problems more visible and the calibration process for behavioural equations more difficult. The growing disaggregation also increases maintenance costs and renders the process of error detection and handling more complicated and thus more resource-intensive. Finally, the enlarged model (with now more than 10.000 equations) raises demands on software and hardware alike.

Table 1Country grouping in @2030

Code / Description
Far East
CHIN / China
INDI / India
JAPA / Japan
PHIL / Philippines
THAI / Thailand
VIET / Vietnam
MYAN / Myanmar
SASI / South Asia (Bangladesh, Sri Lanka, Nepal, Maldives)
EASI / East Asia (remaining Asian countries)
Northern Africa and Near East
ALGE / Algeria
MORO / Morocco
TURK / Turkey
EGYP / Egypt
NENA1 / Mediterranean countries 1 (Tunisia, Libya, Lebanon)
IRAN / Iran
NENA2 / Near East remaining, without Israel
The Americas
USA / United States
CANA / Canada
MEXI / Mexico
COLO / Colombia
BRAZ / Brazil
ARGE / Argentina
LATI / Rest of South America
Europe and FSU
EU / European Union
EUPM / Central Europe
OTHE / Other Europe
FSU / Former Soviet Union
Africa
NIGA / Nigeria
KENY / Kenya
CODR / Zaire
TANZ / Tanzania
SUDA / Sudan
SSAF / Sub-Saharan Africa
Rest
ODEV / South Africa and Israel
AUNZ / Australia and New Zealand

Table 2Commodity coverage:

Crops
Cereals / Wheat, milled rice, maize, barley, millet, sorghum and other cereals
Oilseeds / Vegetable oils, oil meals are not yet included
Roots and tubers / Cassava, potatoes, sweet potatoes, yams, and other root crops.
Tropical beverages / Cocoa, coffee, and tea
Other crops / Plantains, sugar (in raw equivalents), pulses, vegetables, bananas, citrus, and other fruits
Animal Products
Meats, livestock, and livestock products / Beef, pig meat, poultry meat, milk and milk products, eggs, camel-sheep-goat meat
Other food products
Product category for commodities not accounted for in any of the above categories. In many countries, the single most important item of this category is fish and fish products
Non-food commodities
Tobacco, cotton lint, other fibre plants, and rubber

All commodity data are expressed in primary product equivalent unless stated otherwise. Historical commodity balances (Supply Utilization Accounts SUAs) are available for about 160 primary and 170 processed crop and livestock commodities. To reduce this amount of information to manageable proportions, all the SUA data have been converted to the commodity specification given above in the list of commodities, applying appropriate conversion factors (and ignoring joint products to avoid double counting: e.g. wheat flour is converted back into wheat while wheat bran is ignored). In this way, one Supply Utilization Account in homogeneous units is derived for each commodity in the model. Meat production refers to indigenous meat production, i.e. production from slaughtered animals plus the meat equivalent of live animal exports minus the meat equivalent of all live animal imports. Cereal demand and trade data include the grain equivalent of beer consumption and trade.

3.Price system

3.1.Overview

The model is based on the assumption of homogenous goods. Price differences between regions reflect hence costs - transport and transaction costs, import tariffs and export tariffs etc. – and not differences in quality or consumer preferences. Accordingly, goods produced or consumed in any one regions are perfect substitutes for goods produced and consumed in any other one[6].

Regarding the link between regional and world markets, the @2030 modelling system differentiates between two “types” of regions:

  1. Regions where price transmission to world market depends on policy induced price wedges according to OECD’s PSE/CSE concept. PSE/CSE data are mostly available for developed countries, where efficient transport and marketing system link internal market prices for standard qualities almost perfectly to border prices if policies do not interfere. Accordingly, the only dampening factor in the model regarding transmission of world policy changes into these regions are policy interventions by tariffs and domestic support.
  2. Regions, where the price transmission is modelled based on iso-elastic price-transmission equations. In most cases, these regions – where PSE/CSE data are missing – represent groups of or individual developing countries. The price transmission elasticities are a broad measure how deficits in transport and marketing infrastructure as well as policy interventions insulate domestic markets from international ones. A paragraph below discusses how the price transmission elasticities are generated.

The prices and price elements in the system are shown in the following table, all measured in constant US$.

Table 3Price system

Price/Price element / Code / Explanation / Endogenous
Uniform world market price / WorldPrice / Central price anchor in the system which drives all other prices / Yes
Price wedge / PWedge / Difference between uniform world market and border prices, only present for regions with PSE data, lines up PSE reference price with uniform world market price in data base / No
Border price / BordPrice / Linked to uniform world market price via fixed price wedge (transport and transaction costs) / Yes
PSE market / PSEm / Market price support according to OECD definition, linear additive element / NO
Farm gate price / FGatPrice / Raw product price at farm gate, either linked via price transmission elasticity or PSEm to border price / Yes
Prices for energy and protein / FgatPrice,Ener
FgatPrice,Prot / Shadow prices for energy and protein balances for feed requirements and deliveries / YES
Producer incentive prices / PIncPrice / Raw product price driving production allocation, farm gate price plus non-market PSE minus energy/protein requirements valued with energy/protein price / YES
Feed use incentive prices / FeedPrice / Farm gate price of raw product minus energy/protein content of feedstuff valued with energy/protein price / YES
Consumer price margin / ConsPMarg / Fixed margin representing processing and marketing costs to convert raw to final products / NO
Consumer prices / ConsPrice / Consumer price per ton of raw product equivalent, equal to farm gate prices plus consumer price margin / YES

3.2.Consumer prices

Unlike many other partial equilibrium models, @2030 clearly distinguishes producer and consumer prices. This distinction was deemed necessary to capture the growing dichotomy between prices at the producer and consumer level, largely caused by a growing importance of services at the processing and marketing level. In the course of economic development, services and production processes link to providing food are moved from the household and the village in highly developed production and marketing chains. Whereas a villager in Sub-Saharan Africa may crop, harvest, store, mill by hand, make dough from the resulting flour, collect fire wood and heat his own oven to produce bread, a New Yorker orders a Sandwich to be delivered to the work place. Accordingly, the reaction of both consumers to changes in the price of the raw product may be quite different. We may doubt that the New Yorker will ever notice that market prices for grains fluctuate if he uses the price of his Sandwich as the indicator. For the African villager, differences between raw product prices and consumer pries are however almost non-existent and his consumption and farming decisions are very closely linked.

Whereas data on food expenditure for at least groups of goods, as well as prices for food items as bread and pasta are provided by official statistics in developed countries, data for developing countries are scarce. The ILO collects consumer prices in order to construct price indices, and that source was checked for suitability to derive consumer prices for the @2030 framework. Results were not really inviting. Prices even for neighbouring countries in Africa often fluctuated by more than the factor of ten when converted to US$. Clearly, converting price notations from local currencies in developing countries requires in depth knowledge regarding the economic system, so the result was not really astonishing. Given the limited scope of the study, the approach had to be abandoned[7].

Instead, a heuristic process was chosen, making use of available statistical data for European countries. Margins relative to farm gate prices for developed countries were derived from that sources and set as shown in the table below. They were used for the region with the highest per capita income. The last line shows the relative margin used for a region with a per capita income of $500US. Consumer price margins for any country are constructed by interpolation of between these two points.

Table 4Relative consumer price margins at minimum at maximum income per capita

Rice / Cereals / Sugar / Other crops / Milk & milk products / Other animal products
CMr,max / 1 / 10 / 5.0 / 5.0 / 2.0 / 1.0
CMr,min / 0.3 / 0.5 / 0.8 / 0.2 / 0.4 / 0.2

The following graph shows the development of farm prices and consumer price for wheat in China in relation to the projected growth in GDP per caput.

Figure 1Margins between producer and consumer prices

For developed countries, high processing and marketing margins for food products largely insulate food demand from changes in agricultural raw product prices, both through low budget shares of food expenditure in total demand and small value shares of raw products in the final (retail) value of food. The resulting large difference between the primary agricultural product and the final retail good functions as a buffer for swings in prices of primary products and makes demand for the final products inelastic with respect to changes in primary products. The difference between primary and final products typically rises with the stage of development and makes therefore food demand in developed countries very price inelastic. Given the time horizons of 15, 30, 50 years, it was deemed necessary to capture and endogenize this difference as a function of overall economic development (GDP)[8].