U.S. patent application number 09/775946 was filed with the patent office on 2002-10-03 for method for forecasting the effects of trade policies and supply and demand conditions on the world dairy sector.
This patent application is currently assigned to Wisconsin Alumni Research Foundation. Invention is credited to Chavas, Jean-Paul, Cox, Thomas L., Zhu, Yong.
Application Number | 20020143604 09/775946 |
Document ID | / |
Family ID | 25106022 |
Filed Date | 2002-10-03 |
United States Patent
Application |
20020143604 |
Kind Code |
A1 |
Cox, Thomas L. ; et
al. |
October 3, 2002 |
Method for forecasting the effects of trade policies and supply and
demand conditions on the world dairy sector
Abstract
The present invention comprises a methodology for forecasting
the effects of domestic and international trade policies on future
trends in world dairy trade on an annualized as well as longer-term
basis. The spatial hedonic equilibrium model employed in the
present invention is used to analyze world dairy sector data and to
forecast future trends by simulating the regional market
equilibrium impacts of trade policies in the world dairy sector.
The model reflects both vertical (e.g. the processing of farm milk
into many different dairy products processing that reflects the
allocation of milk components (e.g., milkfats, caseins, whey
proteins and lactose) to various dairy commodities including
primary, intermediate and processed commodities) and spatial
characteristics (e.g. the distribution of milk production, demand
and trade for dairy products in different regions of the world).
Both domestic and trade policies, and their variations among
countries, are incorporated in the model. The analysis forecasts
the effects of trade liberalization on attributes of the world
dairy sector (including prices, production, consumption, trade
flows and the welfare of producers, consumers and taxpayers in
various countries).
Inventors: |
Cox, Thomas L.; (Madison,
WI) ; Chavas, Jean-Paul; (Madison, WI) ; Zhu,
Yong; (Schaumburg, IL) |
Correspondence
Address: |
PATRICIA SMITH KING
SUITE 22
222 NORTH MIDVALE BOULEVARD
MADISON
WI
537055072
|
Assignee: |
Wisconsin Alumni Research
Foundation
|
Family ID: |
25106022 |
Appl. No.: |
09/775946 |
Filed: |
February 2, 2001 |
Current U.S.
Class: |
705/7.31 ;
705/7.25; 705/7.34; 705/7.36; 705/7.37 |
Current CPC
Class: |
G06Q 30/02 20130101;
G06Q 10/0637 20130101; G06Q 30/0206 20130101; G06Q 30/0205
20130101; G06Q 10/06315 20130101; G06Q 10/06375 20130101; G06Q
30/0202 20130101 |
Class at
Publication: |
705/10 |
International
Class: |
G06F 017/60 |
Claims
We claim:
1. A method of forecasting the effects of a plurality of trade
policy and supply and demand scenarios on a plurality of attributes
of the world dairy sector across a plurality of regions, the method
comprising: creating a database of world dairy sector data, the
data comprising a plurality of factors pertaining to dairy primary,
intermediate and processed commodities including components
thereof; refining an hedonic spatial equilibrium model of the world
dairy sector using the world dairy sector data; and, running the
refined model under the plurality of scenarios to forecast the
effects of each of said scenarios on the world dairy sector
attributes on at least an annualized basis.
2. The method of claim 1, wherein said plurality of attributes of
the dairy sector comprise prices, production, consumption, trade
flows and welfare of producers, consumers and taxpayers.
3. The method of claim 1, wherein said plurality of regions
comprise the United States, Mexico, China (including Hong Kong,
Taiwan, Macao and Mongolia), India, Japan, Australia, New Zealand,
western Europe (all western European countries including Malta),
eastern Europe, the former Soviet Union countries, Korea (north and
south), Southeast Asia (countries to the east of and including
Myanmar), Other South Asian countries, Middle East (including
Cyprus), North Africa, Republic of South Africa, Canada, South
America (excluding Argentina, Chile, Uruguay), South America
(Argentina, Chile, Uruguay), Central America and Caribbean
countries (excluding Mexico) and a remainder category of mostly
Sub-Saharan Africa countries.
4. The method of claim 1, wherein said primary commodities comprise
cow, buffalo, camel, sheep and goat milk and said components
comprise fats, casein proteins, whey proteins, other nonfat solids
and further fractionations thereof.
5. The method of claim 1, wherein said processed commodities
comprise cheeses, butters, whole milk powders, skim milk powders,
dry wheys, caseins, condensed milks, evaporated milks and other
dairy products.
6. The method of claim 1, wherein said intermediate commodities
comprise butters, skim milk powders, whole milk powders, condensed
milks, evaporated milks, caseins, dry wheys, milk protein
concentrates and other products embodying fractionated milk
components.
7. The method of claim 1, wherein one of said plurality of
scenarios comprises a base scenario to reflect recent world
economic conditions.
8. A method of forecasting the effects of a plurality of trade
policy and supply and demand scenarios on a plurality of attributes
of the world dairy sector across a plurality of regions, the method
comprising: creating a database of world dairy sector data, the
data comprising a plurality of factors pertaining to dairy primary,
intermediate and processed commodities including components
thereof, creating the database comprising: compiling the data;
transforming the data to be usable by an hedonic spatial
equilibrium model of the world dairy sector; and, updating the
data; refining the model using the world dairy sector data,
refining the model comprising: running the model under a base
scenario to forecast the plurality of world dairy sector attributes
under a set of recent world economic conditions; calibrating a
portion of the data, said portion comprising at least price data;
re-running the model using the calibrated data; and, validating the
model; and, running the refined model under the plurality of
scenarios, comprising the base scenario and a plurality of non-base
scenarios, to forecast the effects of each of said non-base
scenarios on the world dairy sector attributes on an annualized
basis as a difference between a scenario's forecast and the base
forecast.
9. The method of claim 8, wherein said plurality of attributes of
the dairy sector comprise prices, production, consumption, trade
flows and welfare of producers, consumers and taxpayers.
10. The method of claim 8, wherein said plurality of regions
comprise the United States, Mexico, China (including Hong Kong,
Taiwan, Macao and Mongolia), India, Japan, Australia, New Zealand,
western Europe (all western European countries including Malta),
eastern Europe, the former Soviet Union countries, Korea (north and
south), Southeast Asia (countries to the east of and including
Myanmar), Other South Asian countries, Middle East (including
Cyprus), North Africa, Republic of South Africa, Canada, South
America (excluding Argentina, Chile, Uruguay), South America
(Argentina, Chile, Uruguay), Central America and Caribbean
countries (excluding Mexico) and a remainder category of mostly
Sub-Saharan Africa countries.
11. The method of claim 8, wherein said primary commodities
comprise cow, buffalo, camel, sheep and goat milk and said
components thereof comprise fats, casein proteins, whey proteins,
other nonfat solids and further fractionations thereof.
12. The method of claim 8, wherein said processed commodities
comprise cheeses, butters, whole milk powders, skim milk powders,
dry wheys, caseins, condensed milks, evaporated milks and other
dairy products.
13. The method of claim 8, wherein said intermediate commodities
comprise butters, skim milk powders, whole milk powders, condensed
milks, evaporated milks, caseins, dry wheys, milk protein
concentrates and other products embodying fractionated milk
components.
14. The method of claim 8, wherein one of said plurality of
scenarios comprises a base scenario to reflect recent world
economic conditions.
15. A method of forecasting the effects of a plurality of trade
policy and supply and demand scenarios on a plurality of attributes
of the world dairy sector, the method comprising: running an
hedonic spatial equilibrium model of the world dairy sector, the
model inputting data from a database of world dairy sector data,
the data comprising a plurality of factors pertaining to a
plurality of d airy commodities, said commodities including
primary, intermediate and processed dairy commodities and
components thereof; refining the model using the world dairy sector
data, refining the model comprising: running the model under a base
scenario to forecast the plurality of world dairy sector attributes
under a set of recent world economic conditions; calibrating a
portion of the data, said portion comprising at least price data;
re-running the model using the calibrated data; and, validating the
model; and, running the refined model under the plurality of
scenarios, comprising the base scenario and at least one of a
plurality of non-base scenarios, to forecast the effects of the at
least one non-base scenarios on the world dairy sector attributes
on an annualized basis as a difference between said at least one
non-base scenario's forecast and the base forecast.
16. The method of claim 15, wherein running the spatial hedonic
model comprises: calculating an amount of surplus across the
plurality of dairy commodities and a plurality of geographic
regions by adding producer and consumer surplus; subtracting a cost
of transporting and processing said plurality of dairy commodities
across the regions; subtracting a value reflecting the net effects
of a plurality of classical trade distortions; modifying the
foregoing analysis by a set of values reflecting price distortions
and quantity restrictions generated by the at least one non-base
scenario; and, subjecting the preceding calculations to at least
one of a plurality of constraints dependent on the at least one
policy scenario.
17. In a method of forecasting the effects of a plurality of trade
policy and supply and demand scenarios on a plurality of attributes
of the world dairy sector by running an hedonic spatial equilibrium
model of the world dairy sector, an improvement to further optimize
the model results comprising: incorporating into the model a
plurality of factors pertaining to a plurality of intermediate
dairy commodities that may be reconstituted for use in the
production of a plurality of final dairy commodities, the plurality
of factors comprising: cost of processing the intermediate
commodities into the final commodities; shipments of the
intermediate commodities under within and over quota tariffs and
export subsidies; expanded component balance incorporating the
conversion of the intermediate commodities into final commodities;
and, expanded trade balance, import quota, export subsidy and
non-negativity constraints of the model that include the
intermediate and reconstituted final commodities and trade flows;
whereby milk reconstitution technology is reflected in the
model.
18. A method of modeling the regional effects of a plurality of
trade policy and supply and demand scenarios on a plurality of
attributes of the world dairy sector by solving for a market
equilibrium value over a plurality of regions under at least one of
said policy scenarios, the method comprising: calculating an amount
of surplus across a plurality of dairy commodities and the
plurality of regions by adding producer and consumer surplus, the
plurality of dairy commodities comprising primary, intermediate and
processed commodities; subtracting a cost of transporting and
processing said plurality of commodities across the regions;
subtracting a value reflecting the net effects of a plurality of
classical trade distortions; modifying the foregoing analysis by a
set of values reflecting price distortions and quantity
restrictions generated by the at least one of said policy
scenarios; and, subjecting the preceding calculations to at least
one of a plurality of constraints dependent on the at least one
policy scenario.
19. The method of claim 18, wherein said plurality of regions
comprise the United States, Mexico, China (including Hong Kong,
Taiwan, Macao and Mongolia), India, Japan, Australia, New Zealand,
western Europe (all western European countries including Malta),
eastern Europe, the former Soviet Union countries, Korea (north and
south), Southeast Asia (countries to the east of and including
Myanmar), Other South Asian countries, Middle East (including
Cyprus), North Africa, Republic of South Africa, Canada, South
America (excluding Argentina, Chile, Uruguay), South America
(Argentina, Chile, Uruguay), Central America and Caribbean
countries (excluding Mexico) and a remainder category of mostly
Sub-Saharan Africa countries.
20. The method of claim 18, wherein said primary commodities
comprise cow, buffalo, camel, sheep and goat milk and said
components thereof comprise fats, casein proteins, whey proteins,
other nonfat solids and further fractionations thereof.
21. The method of claim 18, wherein said processed commodities
comprise cheeses, butters, whole milk powders, skin milk powders,
dry wheys, caseins, condensed milks, evaporated milks and other
dairy products.
22. The method of claim 18, wherein said intermediate commodities
comprise butters, skim milk powders, whole milk powders, condensed
milks, evaporated milks, caseins, dry wheys, milk protein
concentrates and other products embodying fractionated milk
components.
23. The method of claim 18, wherein said cost of transporting and
processing is calculated by adding a cost of transforming said
primary commodities into said intermediate commodities, a cost of
processing said intermediate commodities into said processed
commodities, a cost of transporting and marketing said primary
commodities between regions, and a cost of transporting and
marketing said intermediate and said processed commodities between
regions.
24. The method of claim 18, wherein said plurality of classical
trade distortions comprise within and over quota tariffs, export
subsidies and production and import quotas.
25. The method of claim 18, wherein said plurality of constraints
dependent on the at least one policy scenario comprise component
balance, trade balance, import quotas, export subsidies, trade
flows and non-negativity constraints.
Description
BACKGROUND
[0001] The present invention relates generally to economic models
to forecast the effects of trade policies and supply and demand
trends on market sector pricing and shares, and in particular to an
hedonic spatial equilibrium trade model that accommodates
interregional variations, multiple products, and implicit markets
for milk components (e.g., milkfat, casein, whey protein and
lactose as allocated to various dairy commodities including
primary, intermediate and processed commodities) to generate
annualized and longer-term forecasts of the effects of trade
policies and supply and demand conditions on attributes of the
world dairy sector (including prices, production, consumption,
trade flows and the welfare of producers, consumers and taxpayers
in various countries).
[0002] Historically the U.S. dairy sector has been a minor player
in world dairy markets. Over the 1989-94 period, for example, the
U.S. exported the equivalent of only 2.5% of total domestic milk
production while accounting for 6% of the total world dairy exports
(excluding intra-European Community trade). Evolving world trade
liberalization, especially the completion of the General Agreement
on Tariffs and Trade (GATT) Uruguay Round Agreement (URA), is
changing this situation. The U.S. dairy sector is increasingly
integrated into a global dairy economy characterized by increased
private exports of U.S. dairy products, increased dairy imports,
less government intervention, and additional foreign investment in
the U.S. dairy industry.
[0003] This changing dairy trade environment offers the U.S.
opportunities to expand dairy exports, as well as further opening
domestic markets to imports from the rest of the world. To better
understand the impacts of global trade liberalization on the
competitiveness of the U.S. dairy sector in these markets,
additional knowledge of international dairy markets and improved
policy modeling capabilities are needed to help the U.S. dairy
sector adjust effectively to the new environment.
[0004] With this motivation, the original goal of the present
inventors was to improve world dairy sector policy modeling
capabilities and to provide a detailed, quantitative assessment of
the impacts of trade liberalization, especially the Uruguay Round
of the GATT, on world dairy markets and the U.S. dairy sector.
While the literature on trade liberalization is vast, comprehensive
and systematic studies on world dairy markets, both in regional and
in commodity detail, have been quite limited.
[0005] The present inventors accomplished their initial general
goal by (a) conducting a comprehensive survey of the world dairy
situation at a twenty-one region level; (b) assessing and
summarizing the then current trade liberalization agreements,
especially the URA of the GATT, for their potential impacts on
world dairy markets; (c) assessing the literature to obtain
insights on supply/demand trends and agricultural/trade policy for
the U.S. and major dairy producing/consuming and
exporting/importing regions; (d) using the insights and parameters
from (a)-(c), designing, building and calibrating a world dairy
trade model including twenty-one regions and nine dairy product
markets; and, (e) summarizing and evaluating the farm/wholesale
impacts of alternative trade liberalization scenarios and
demand/supply growth conditions on the U.S. dairy sector.
[0006] These initial objectives were met and resulted in a world
dairy trade model capable of forecasting the effects of various
domestic and international trade policies and supply and demand
trends on world dairy trade in three to five year trends (Cox, et
al., An Economic Analysis of the Effects on the World Dairy Sector
of Extending Uruguay Round Agreement to 2005, Can. J. of Agr. Econ.
47 (1999)169-183; and, Zhu, et al., An Economic Analysis of the
Effects of the Uruguay Round Agreement and Full Trade
Liberalization on the World Dairy Sector, Can. J. of Agr. Econ. 47
(1999)187-200). However, further refinement of the data and model
was required to improve the accuracy of those predictions and to
allow more detailed annualized trend reporting.
[0007] The present invention addresses these problems by providing
a refined methodology for creating a database of world dairy sector
information sufficient to the task and for modeling the effects of
domestic and international trade policies and supply and demand
trends on future trends in world dairy trade on an annualized as
well as longer-term basis. The spatial equilibrium model employed
in the present invention is used to analyze the data and to
forecast future trends by simulating the regional market
equilibrium impacts of trade policies in the world dairy sector.
The model reflects both vertical (e.g. the processing of farm milk
into many different dairy products, processing that reflects the
allocation of milk components to various dairy commodities,
including primary, intermediate and processed commodities) and
spatial (e.g. the distribution of milk production, demand and trade
for dairy products in different regions of the world)
characteristics. Both domestic and trade policies (and their
variations among countries), as well as supply/demand trends and
exchange rate changes, are incorporated in the model. The analysis
forecasts the effects of trade liberalization on attributes of the
world dairy sector (dairy prices, production, consumption, trade
flows and the welfare of producers, consumers and taxpayers in
various countries). The forecasts are generated on an annual, as
well as longer-term basis, providing information regarding various
attributes of the world dairy sector valuable to businesses
involved in the U.S. and other regional dairy sectors. The world
dairy price and trade flow forecasts provide valuable information
that can be used by businesses to compete in the world dairy
market, and by governments in policy negotiations.
[0008] In the accompanying drawings:
[0009] FIG. 1 is a flow diagram depicting the general steps in the
method of the present invention;
[0010] FIG. 2a is a flow diagram of the allocation process of
primary and processed commodities;
[0011] FIG. 2b is a flow diagram of the allocation process of
primary, intermediate and processed commodities;
[0012] FIG. 3 is a sample of an annualized forecast over a period
of 5 years, including validations;
[0013] FIG. 4 is a sample comparison of the regional forecasted
milk price impacts under various alternative policy scenarios;
and,
[0014] FIG. 5 is a sample comparison of the regional forecasted
maximum allowable subsidized exports under various alternative
policy scenarios.
DESCRIPTION
[0015] Referring now to the figures, in which identical or similar
steps are designated by the same reference numerals throughout, a
detailed description of various alternative embodiments of the
present invention is given. However, the present invention can
assume additional embodiments, as will become apparent to those
skilled in the art, without departing from the appended claims.
[0016] Referring to FIG. 1, the steps in the method of the present
invention generally comprise (1) creating a database of world dairy
sector data 100, (2) refining the model 200, and (3) running the
refined and updated model under various policy scenarios to
forecast the effects of each of the scenarios on the world dairy
sector attributes 300 (see FIG. 1). The descriptions of these basic
steps are preceded by a description of the spatial equilibrium
model and policy scenarios of the present invention, since it will
be referred to throughout the remainder of this section.
[0017] Multiple modes of implementation of the method of this
invention are possible. For example, the method may be implemented
in a variety of programming languages on a variety of computer
systems. It may be implemented using pre-packaged software or
customized programming. Portions of the database compilation step
may involve the downloading of data over the Internet, retrieval
from a form of electronic storage media and/or input by hand.
[0018] The Model and Policy Scenarios.
[0019] The hedonic spatial equilibrium model employed in the
present invention is a model of the world dairy markets. The model
is a static, spatial, multi-product, multi-component (hedonic)
framework of the world dairy sector with vertical linkages among
production stages. It is used to analyze the data and to forecast
future trends by simulating the regional market equilibrium impacts
of trade policies in the world dairy sector. The analysis considers
many separate regions of the world, including the U.S., Canada,
Mexico, China, India, Japan, Australia, New Zealand, western
Europe, eastern Europe and the former Soviet Union (FSU). The model
considers five types of farm milk (cow, buffalo, camel, sheep and
goat) embodying several milk hedonic characteristics (fats, casein
proteins, whey proteins, other nonfat solids (lactose, salts, other
minerals and ash) and further fractionations thereof) that can be
processed into eight types of dairy products (cheeses, butters,
whole milk powders, skim milk powders, dry wheys, caseins,
evaporated/condensed milks, and other dairy products).
[0020] Notation:
[0021] Consider a vertical sector involving primary commodities
used in the production of processed commodities that are eventually
consumed in I regions. Each region is involved in the production,
trade and utilization of the primary as well as processed
commodities (see FIG. 2a). Let w.sub.i (x.sub.i) be the vector of
primary commodities produced (utilized) in region i, i=1, . . . ,
I. And let y.sub.i (z.sub.i) the vector of processed commodities
produced (utilized) in region i, i=1, . . . , I. All the primary
and processed commodities can be traded between regions. Denote by
T.sub.ij.gtoreq.0 (t.sub.ij.gtoreq.0) the vector of export of
primary commodities from region i to region j. And let C.sub.ij
(c.sub.ij) be the vector of transportation and marketing cost per
unit of primary (processed) commodities traded from region i to
region j.
[0022] Processing Technology:
[0023] Dairy manufacturing is a multi-output process with different
products being produced jointly. It is assumed that there are two
kinds of inputs used to produce the processed commodities y in each
region: the vector of primary commodities x, and other inputs
denoted by the vector .omega..sub.i (e.g., labor, capital). In the
i-th region, the use of inputs .omega..sub.i must satisfy
(.omega..sub.i, x.sub.i, y.sub.i) .epsilon.T.sub.i, where T.sub.i
is the production possibility set. Efficient use of the inputs
.omega..sub.i under perfect competition requires that they be
chosen in a cost minimizing way:
G.sub.i(x.sub.i,
y.sub.i)=min.sub.v{r.sub.i'.omega..sub.i:(.omega..sub.i, x.sub.i,
y.sub.i).epsilon.T.sub.i}, (1)
[0024] where r.sub.i is the vector of market prices for v.sub.i in
the i-th region. G.sub.i(x.sub.i, y.sub.i) in (1) is a cost
function measuring the cost of optimal use of inputs .omega..sub.i,
conditional on primary inputs x.sub.i and output levels
y.sub.i.
[0025] In the context of the dairy sector, the primary commodities
(five types of farm milk comprised of four milk components) can be
transformed into eight processed dairy products (cheeses, butters,
whole milk powders, skim milk powders, dry wheys, caseins,
evaporated/condensed milks, and other dairy products). The crucial
linkages between primary and processed products are the milk
components (milk fats, caseins, whey proteins, other non-fat solids
and further fractionations thereof) that are "rearranged" by dairy
processing plants. In each region, the total amount of components
found in processed products must come from the primary products. To
the extent that each product has fixed composition, this means that
the processing technology can be represented by a Leontief
technology with respect to milk components. Let a.sub.iS (b.sub.iS)
denote the matrix of quantities of the s-th component per unit of
the primary (processed) commodities in the i-th region. And let
matrix A.sub.i denote [a.sub.i1, . . . , a.sub.iS] and B.sub.i
denote [b.sub.i1, . . . , b.sub.iS], where S is the number of
components. Then the transformation relationship between primary
and processed goods in region i must satisfy
B.sub.i'y.sub.i.ltoreq.A.sub.i'x.sub.i, i=1, . . . , I, (2)
[0026] This is a Lancasterian specification establishing fixed
proportion relationships between products and their components,
where the components are perfect substitutes across commodities.
Under the Leontief specification, G.sub.i(x.sub.i, y.sub.i) can be
written as g.sub.i(y.sub.i) plus component balance restrictions
(equation (2)).
[0027] Market Equilibrium:
[0028] In certain settings, market equilibrium is obtained through
the maximization of a net social payoff function given by the sum
of producer and consumer surplus across commodities as well as
regions, net of transportation and processing costs. In a vertical
sector involving more than one stage of production, the cost of
transformation in each stage also needs to be subtracted. This
gives the following quasi-welfare function
V(w,x,y,z,T,t)=.SIGMA..sub.iCS.sub.i(z.sub.i)+.SIGMA..sub.iPS.sub.i(w.sub.-
i)-.SIGMA..sub.ig.sub.i(y.sub.i)-.SIGMA..sub.i,jT.sub.i,jC.sub.i,j-.SIGMA.-
.sub.i,jt.sub.i,jc.sub.i,j. (3)
[0029] where CS.sub.i(z.sub.i) is consumer surplus in region i,
PS.sub.i(w.sub.i) is producer surplus for the primary commodity
w.sub.i in region i, g.sub.i(y.sub.i) is transformation
(processing) cost of final secondary products in region i.
[0030] Assume that the quasi-welfare function V(w, x, y, z, T, t)
is concave and satisfies
.differential.CS.sub.i(z.sub.i)/.differential.z.sub-
.i=p.sub.i.sup.c and
.differential.PC.sub.i(w.sub.i)/.differential.w.sub.i-
=p.sub.i.sup.s, where p.sub.i.sup.c(p.sub.i.sup.s) is the vector of
market prices for the processed (primary) commodities. This assumes
that, under competition, market prices reflect marginal benefits
for consumers and marginal costs for producers. In the presence of
trade, the maximization of aggregate net social payoff is subject
to two sets of constraints: the trade flow constraints and
non-negativity constraints. For the i-th region, the trade flow
constraints are
w.sub.i.gtoreq..SIGMA..sub.jT.sub.ij, (4a)
.SIGMA..sub.jT.sub.ji.gtoreq.x.sub.i, (4b)
y.sub.i.gtoreq..SIGMA..sub.jt.sub.ij, (4c)
.SIGMA..sub.jt.sub.ji.gtoreq.z.sub.i. (4d)
[0031] These restrictions state that exports plus domestic uses
cannot exceed domestic production, and that domestic consumption
cannot exceed domestic production plus imports. This is true for
primary commodities (equations (4a) and (4b)) as well as processed
commodities (equations (4c) and (4d)).
[0032] The optimization problem representing spatial competitive
equilibrium then is
max.sub.w,x,y,z,T,t{.SIGMA..sub.iCS.sub.i(z.sub.i)+.SIGMA..sub.iPS.sub.i(w-
.sub.i)-.SIGMA..sub.ig.sub.i(y.sub.i)-.SIGMA..sub.ijT.sub.ijC.sub.ij-.SIGM-
A..sub.ijt.sub.ijc.sub.ij: subject to equations (2) and (4);
(w,x,y,z,T,t).gtoreq.0} (5)
[0033] In the absence of government intervention (i.e., no
tax/subsidy and no quota distortions), the optimization problem (5)
generates a Pareto efficient resource allocation. It also generates
a competitive market equilibrium where the Lagrange multipliers
associated with constraints (4) are interpreted as market
prices.
[0034] Incorporating Government Policies:
[0035] The next step is to introduce policy parameters in the above
model to reflect domestic and trade policies. The incorporation of
specific duties (i.e., import tariffs and export subsidies) is
straightforward in that they are equivalent to changes in
transportation costs. However, the modeling of ad valorem tariffs
is a little more complex. A simple way is to translate ad valorem
tariffs into equivalent specific duties using observed prices. The
drawback of this approach is that, in a market equilibrium
framework, import tariffs influence market prices. This suggests a
need to treat market prices as endogenous in the calculation of
tariffs. This is done here by solving for market equilibrium
iteratively, where each iteration uses updated specific duties
equivalent of the ad valorem tariffs, until convergence is
obtained. Upon convergence, the solution is identical to the one
obtained from solving directly the associated mixed complementarity
problem. Finally, most non-tariff barriers influence import volume
directly and can be introduced easily in spatial trade models by
adding appropriate restrictions on quantities traded.
[0036] The tariff-rate quota policy is modeled by introducing
two-tiered tariff restrictions. The basic idea is to divide imports
of a commodity into two parts: one is imported at the in-quota
(lower) tariff rate; and the other is imported at the over-quota
(higher) tariff rate. The sum of these two parts is then available
either as consumption or as inputs for further processing. Import
quotas are always filled first at the lower in-quota rate before
importing the commodity at over-quota tariff rates.
[0037] The restrictions on export subsidies are dealt with in a
similar way. For each country, subsidized exports of a particular
commodity are subject to a quantitative restriction, i.e., the
maximum allowable volume subject to subsidies under the GATT. A
country's subsidized exports may also be subject to another
constraint: the maximum allowable budgetary outlays that the
country can spend on export subsidies for a commodity or a group of
commodities. A country will always use up its export subsidy
"quota" before exporting with no subsidy.
[0038] Domestic government programs include price support programs,
production quotas and classified pricing. Price supports can be
modeled by introducing a government sector (funded by tax-payers)
with a perfectly elastic demand at the price support level. Milk
production quotas are handily modeled by adding appropriate
constraints to farm milk production and adjusting farm level milk
prices (the marginal cost of production) as market milk prices
minus milk quota rents. If over-quota taxes are not too
prohibitive, then a two-tier pricing scheme is needed for modeling
domestic production (i.e., using a within- and over-quota pricing
scheme in a way similar to the two-tier pricing discussed above).
Classified pricing is modeled by introducing appropriate price
wedges for the relevant products (e.g., fluid milk).
[0039] The following notation is used to incorporate these
government policies into (5). Let .PI..sub.ij (.pi..sub.ij) be the
vector of unit-tariffs imposed on imports of primary (processed)
commodities from region i to region j, and .DELTA..sub.ij
(.delta..sub.ij) be the vector of unit-subsidy towards exports of
primary (processed) commodities from region i to region j. The
vector of import quotas for the primary (processed) commodities in
region i, i=1, . . . , I, is denoted by Q.sub.i (q.sub.i). Finally,
let S.sub.i (s.sub.i) be the vector of maximum allowable volume of
subsidized exports for the primary (processed) commodities in
region i, i=1, . . . , I.
[0040] In the context of a two-tiered pricing scheme, let the
superscript IQ denote in-quota, OQ denote over-quota import
restrictions, and superscript s denote subsidized exports. Assuming
that import quotas for each region are pooling quotas (i.e., not
bilateral quotas), the distorted market equilibrium can be
expressed as 1 max w , x , y , z , T , t { i CS i ( z i ) + i PS i
( w i ) - i g i ( y i ) - i , j T ij C ij - i , j t ij c ij - i , j
T ij IQ .PI. ij IQ - i , j ( T ij - T ij IQ ) .PI. ij OQ - i , j t
ij IQ ij IQ - i , j ( t ij - t ij IQ ) ij OQ + i , j T ij s ij + ij
t ij s ij : subject to T ij IQ T ij , t ij IQ t ij , i j T ij IQ Q
j , i j t ij IQ q j , j i T ij s S i , j 1 t ij s s i , equations (
2 ) and ( 4 ) ; ( w , x , y , z , T , t , T IQ , t IQ ) 0 } . ( 6
)
[0041] The model (6) represents world dairy markets under domestic
and trade government policies. The first line in (6) is similar to
(5), but expanded to include classical trade distortions (within
and over quota tariffs, export subsidies, and production and import
quotas). The following lines in (6) reflect the price distortions
and quantity restrictions generated by government policies. Model
(6) can be used to investigate empirically the effects of trade
liberalization or other trade policies on the dairy sector.
[0042] Incorporate Intermediate Products:
[0043] Milk reconstitution technology is reflected in the model
with the inclusion of intermediate commodities (see FIG. 2b).
Several categories of products can be used as intermediate dairy
processing commodities (e.g., butters, skim milk powders, whole
milk powders, condensed and evaporated milks, caseins, dry wheys,
milk protein concentrates and other products embodying fractionated
milk components) that may be used in the production of other dairy
products. For example, cream may be considered an intermediate
commodity as it can be further processed into butter, butter oil,
ice cream, buttermilk and many other dairy products. In the dairy
processing practice of milk reconstitution, milk powders, milk fat
products, and other dairy products are converted back into fluid
milk for consumption or are used for making other dairy
products.
[0044] To incorporate the reconstitution technology in the model,
we assume there are two stages in the processing sector. First, the
primary products are converted into intermediate products. At the
second stage, some of the intermediate products are further
processed into final reprocessed products. The other intermediate
products and the reprocessed products compose the final consumption
goods. Trade is possible following the first stage of
processing.
[0045] Suppose a technology allows L types of intermediate
commodities to be reprocessed into M types of final products, which
is a subset of final products. Let u.sub.i be the vector of
intermediate commodities produced in the i.sup.th region and vi be
the vector of intermediate goods available in the i.sup.th region
following the trade. A portion of v.sub.i, vv.sub.i is the vector
of intermediate goods reprocessed into final commodities in the
i.sup.th region, and vector y.sub.i is the output of the
reprocessing procedure. Let G.sub.i (x.sub.i, u.sub.i) be the cost
(i.e., costs of other inputs except for dairy material inputs) of
transforming the x.sub.i of primary goods into u.sub.i of
intermediate goods. Under the Leontief specification,
G.sub.i(x.sub.i, u.sub.i) can be written as g.sub.i(u.sub.i) plus
component balance restrictions. In a similar fashion, let H.sub.i
(vv.sub.i, y.sub.i) be the transformation costs converting vv.sub.i
of intermediate goods into y.sub.i of final commodities, which can
be written as h.sub.i(y.sub.i) plus component balance restrictions.
Let .tau..sub.ij be the shipment of intermediate goods from the
i.sup.th region to the j.sup.th region. Furthermore, let E.sub.i be
the matrix representing the nutrient composition of reconstituted
goods and F.sub.i be the matrix representing the nutrient
composition of intermediate goods.
[0046] The optimization problem (6) with an intermediate product
reprocessing stage is characterized in equation (7) assuming that
reprocessed products share the same trade policies as other
products. 2 max w , x , y , z , T , t { i CS i ( z i ) + i PS i ( w
i ) - i g i ( u i ) - i h i ( y i ) - i , j T ij C ij - i , j ( t
ij + ij ) c ij - i , j T ij IQ .PI. ij IQ - i , j ( T ij - T ij IQ
) .PI. ij OQ + i , j T ij s ij - i , j ( t ij IQ + ij IQ ) ij IQ -
ij ( t ij + ij - t ij IQ - ij IQ ) ij OQ + i , j ( t ij s + ij s )
ij : subject to w i j T ij , j T ji x i , B i ' u i A i ' x i , u i
j ij , j ij v i , E i ' y i F i ' vv i , v i - vv i + y i j t ij ,
j t ji z i , T ij IQ T ij , t ij IQ t ij , ij IQ ij , i j T ij IQ Q
j , i j ( t ij IQ + ij IQ ) q j , j i T ij s S i , j i ( t ij s +
ij s ) s i , and ( w , x , u , v vv , y , z , T , t , , T IQ , t IQ
, IQ ) 0 } . ( 7 )
[0047] Equation 7 extends the optimization problem (6) by
incorporating: 1) the cost of processing intermediate commodities
into final commodities (h.sub.i(y.sub.i)); 2) the shipments of
intermediate commodities (.tau..sub.ij) under within
(.pi..sub.ij.sup.IQ) and over quota (.pi..sub.ij.sup.OQ) tariffs
and export subsidies (.delta..sub.ij); 3) an expanded component
balance incorporating the conversion of intermediate products into
final products (E.sub.i'y.sub.i.ltoreq.F.sub.i'vv.sub.i, noting
that B.sub.i'u.sub.i.ltoreq.A.sub.i'x.sub.i, is equivalent to (2));
and 4) expanding the trade balance
(v.sub.i-vv.sub.i+y.sub.i.gtoreq- ..SIGMA..sub.jt.sub.ij,
.SIGMA..sub.jt.sub.ji.gtoreq.z.sub.i), import quota
(.SIGMA..sub.i.noteq.j(t.sub.ij.sup.IQ+.tau..sub.ij.sup.IQ).ltoreq.-
q.sub.j,), export subsidy
(.SIGMA..sub.j.noteq.i(t.sub.ij.sup.s+.tau..sub.-
ij.sup.s).ltoreq.s.sub.i,) and non-negativity ((w, x, u, v, vv, y,
z, T, t, .tau., T.sup.IQ, t.sup.IQ, .tau..sup.IQ).gtoreq.0)
constraints of (6) to include the intermediate and reconstituted
final products (v.sub.i, vv.sub.i, y.sub.i) and trade flows
(.tau..sub.ij).
[0048] To analyze the effects of these various policies on world
dairy trade, the model is run to provide a BASE scenario 310 (see
FIG. 1) that reflects recent world economic conditions. Using this
BASE scenario, the model is then re-run to simulate the effects of
various policies 320. A number of the possible policy scenarios are
summarized below. It should be noted that several scenarios
describing specific year forecasts are described in the following
by way of example only. The actual years forecasted will change
with each set of model simulations.
[0049] BASE Scenario:
[0050] The model equation (6) is solved using the General Algebraic
Modeling System (GAMS) optimization package (though as noted
previously, alternative computer programs may be used). First, a
model is specified to provide an accurate representation of the
world dairy markets. This gives a BASE scenario that reflects
recent world economic conditions 310. This BASE scenario is then
modified to simulate the effects of various alternative policy
scenarios regarding, for example, GATT commitments and
demand/supply shifts. A series of sensitivity analyses are then
conducted on the BASE model with respect to the magnitude and the
functional form of transportation costs, demand and supply
elasticity parameters, and manufacturing cost specifications. In
order to understand the relative importance of natural trade
barriers (transportation costs) versus man-made trade barriers
(trade distortions) in world dairy trade, several scenarios are
generated with each of the major policy instruments (tariffs,
import quotas, and export subsidies) eliminated and the results
compared with the role of transportation costs.
[0051] After the model is judged to reasonably replicate the data
inputs in the BASE scenario, it is used to simulate the world dairy
situation in several alternative policy scenarios 320. Combining
policy changes with predicted demand/supply changes, a number of
scenarios are then generated to forecast the annualized changes in
the world dairy situation, as well as longer term changes.
[0052] Policy Scenario Simulations:
[0053] When the calibrated BASE model generates solutions that are
reasonably close to data inputs, it is used as the benchmark
against which the results from other simulation scenarios are
compared 340. These simulation scenarios are divided in two major
groups: ceterisparibus policy analyses, and forecasting scenarios.
The first group includes a free market scenario (FM, total
elimination of trade and trade related domestic policies), a
scenario with the trade policies of a certain year under the GATT
(e.g., for the year 2000 under the GATT (GATT 2000)), and one with
both trade and domestic policy changing from the BASE period to the
year selected (e.g. the year 2000 (Policy 2000)). The forecasting
scenarios consist of various combinations of policy changes and
projected exogenous demand/supply changes.
[0054] Ceteris paribus is used to mean comparative static analysis
of policy changes only, given that regional demand/supply curves
are fixed. Three policy scenarios are considered. The first policy
scenario assumes that each GATT member country applies its minimum
trade liberalization obligations in the year 2000, for example,
under the URA (i.e., maximum tariff rates, minimum market accesses,
and maximum allowable export subsidies). This scenario reflects, in
some sense, the pure effects of the URA. The second scenario
analyzed in this section involves GATT trade policy changes as well
as projected domestic dairy policy reforms. These are considered
simultaneously because some domestic policies have to be adjusted
accordingly to meet GATT commitments during the implementation
period of the URA. The third scenario in this section is the Free
Market situation. This scenario is identical to a scenario in the
previous section (i.e., one with full elimination of the status quo
tariffs, import quotas, export subsidies, and related domestic
policies). Unlike tariffs, export subsidy restrictions are
specified in terms of maximum allowable subsidized quantity and
budgetary outlays under the URA. Member countries are free to
choose their subsidy rates as long as they do not violate the
volume and budgetary outlay restrictions. The model assumes that
the countries having export subsidy policies will try to maximize
their export volume during the implementation period of the
URA.
[0055] GATT 2000:
[0056] GATT 2000 refers to the scenario where each GATT member
country fulfills marginally its URA commitments for trade
liberalization by the year 2000 (or other year as appropriate).
Hence, the model assumes that maximum allowable tariff rates and
minimum import quotas under the URA will be the applied trade
policies. Non-World Trade Organization (Non-WTO) members are
assumed to keep their current (or the BASE period) trade policies.
Domestic policies remain at the BASE level, as do the demand and
supply schedules. Thus, this scenario is used to assess the
ceterisparibus effects of the URA of the GATT on the world dairy
sector.
[0057] Policy 2000--Adding Domestic Policy Reforms:
[0058] The pressures for liberalization in world dairy trade come
not only from the multilateral agreement, i.e., GATT, but also from
internal sources in many developed countries. The large budget
burdens of commodity programs in heavily protected dairy sectors
increasingly conflict with the domestic considerations that led to
their extensive adoption.
[0059] In Policy 2000, trade policy changes under the URA and
projected domestic policy changes (for the year 2000, or other year
as desired) are combined. Three types of domestic policies are
assumed to change in this scenario: price supports, production
quotas, and direct dairy subsidies for manufacturing milk
utilization.
[0060] Free Market (FM):
[0061] In the Free Market scenario, the model assumes all tariffs,
import quotas, and export subsidies are eliminated from the BASE.
Domestic farm policies that are closely related to trade, such as
price supports and production quotas, are also eliminated. The only
type of farm policies kept is classified pricing policies in the
U.S., Canada, and Australia. This is an analysis to explore the
foremost potential of trade liberalization in world dairy markets.
It provides important information about the competitiveness of each
region in world dairy markets, and about the potential ultimate
results of the trade liberalization efforts of the GATT (WTO). This
assessment can also serve as a supporting analysis for the future
WTO negotiations.
[0062] Adjustment for Demand/Supply Shifts and Forecasts:
[0063] Income and population are generally considered the most
important determinants for aggregate demand. The linkage between
income/population changes and demand shifts is the income
elasticity (using per capita income) and population elasticity. The
model assumes the population elasticity for all dairy productions
is one, i.e., 1% population growth leads to a 1% increase in total
demand. In a partial equilibrium analysis, income and population
changes are treated as exogenous demand shifters. In this study,
the model assumes parallel demand curves shifts, which means slopes
of demand curves are fixed during the shifts. Generally, the income
elasticity of food products tends to be lower the higher the income
level and the higher the per capita consumption.
[0064] The other set of demand shifter estimates is based on
projected regional gross domestic product (GDP) growth rates (e.g.,
1994-2000, or other ranges as data are available). The World Bank
has already published the GDP growth rates for the first three
years of this period. For the second three years, the forecast data
from other sources is used, especially investment companies, which
publish a variety of GDP growth rate forecasts with consideration
of important macroeconomic factors, such as reform processes and
economic crises.
[0065] Supply shifters are more difficult to identify in sectoral
models. The major determinant, technological change, is hard to
measure directly. An indirect approach may sometimes be used in
which the changes in other production factors are subtracted from
the total production change and a residual computed that is
interpreted as a measure of technological change. This idea also
applies to the estimation of supply shifters in sectoral models. A
change in production can be explained by price changes (movement
along a supply curve) and other changes (supply shifters). Assuming
the production growth rate and price change rate are known, the
supply shifter can be measured as
.DELTA. ln Q-.eta..sub.p.DELTA. ln P (8)
[0066] where .eta..sub.p is the price elasticity of the supply.
[0067] This supply shifter embodies not only the technological
change, but also possible changes in government subsidy, tax
policies and other farm policies. Other factors, such as weather
and input prices, are also likely included in this shifter. Because
several policy changes are explicitly integrated in the model,
using the shifter estimated by equation (8) to forecast the future
world dairy situation might be inappropriate in certain
situations.
[0068] Simulating Demand/Supply Changes without GATT (2000GR):
[0069] The first scenario analyzed in this section assumes no
policy changes over the BASE Scenario and shifts demand/supply
following the historical trends observed during 1989-95 (or other
period as appropriate). This scenario can be considered a new BASE
(referred to as 2000GR, where "GR" stands for "Growth") on which
the assessment of impacts of policy changes is made. Moreover,
comparing the impacts of demand/supply change with the ceteris
paribus policy analyses in the previous section will provide
information about the relative magnitude of policy impacts with
respect to other factors, such as income, population growth and
technological changes.
[0070] Forecasting Year 2000 (2000GRG and 2000GRP; or Other Year as
Appropriate):
[0071] The scenarios combining projected demand/supply changes and
policy changes reflect the model forecasts of the year 2000 world
dairy situation (or other year as appropriate). Two scenarios
(2000GRP and 2000GRG) are implemented for the forecasting purpose
in this study. In both scenarios, GATT member countries use their
marginal policies under their URA commitments (i.e., maximum
tariffs, minimum import quotas, and maximum allowable export
subsidies) for the year 2000. The difference between these two
scenarios is that, in 2000GRP, several domestic dairy policies
change in selected countries (the same as in the Policy 2000
Scenario), while in 2000GRG, domestic farm policies are the same as
in the BASE. In short, 2000GRP is the combination of 2000GR and
Policy 2000, while 2000GRG combines 2000GR and GATT 2000.
[0072] Free Market in 2000 (2000GRFM):
[0073] The 2000GRFM scenario reflects the full trade liberalization
situation in the year 2000 given that the demand/supply shifts
follow historical trends (1989-1995; years may vary with the
particular analysis). Trade related domestic policies (price
supports, production quotas and direct subsidies) are eliminated as
well in this scenario.
[0074] Adjustments to Demand and Supply Shifters:
[0075] In the previous forecast scenarios, the demand and supply
shifters are projected from the trends in historical data (ie.,
1989 to 1996, or other period of time as appropriate). This type of
simple projection approach can be useful in general, but it is
quite naive. Forecasts based on adaptive expectations do not
consider what has happened recently and what will happen in the
future, and consequently, should be treated cautiously. For
example, in Eastern Europe and FSU, the GDP growth rate was about
-5% a year during the BASE period, when the countries in these
regions started economic reforms. These economies have become more
stable and positive GDP growth rates have been observed recently in
many of these countries. As a result, a minus five percent growth
rate is definitely not a good projection of growth rate for the
period of 1994-2000. The current financial and economic crisis in
East Asian countries will reduce the GDP growth rates in affected
countries significantly, due to the contagion effects on the rest
of the world. A similar situation exists in the forecasts for
regional milk supply shifters in the regions with sharp declines in
milk production in the BASE period due to various macroeconomic
factors that are expected to disappear in the future.
[0076] Under these considerations, a rational expectation approach
where new information being used could be more appropriate than the
adaptive expectation (where only historical trends are used) to
predict demand/supply changes. A set of modified projections based
on real GDP growth is constructed. For countries without
forecasting information, the historical data is still used. Changes
are made mostly on Organization for Economic Cooperation and
Development (OECD) countries and important emerging markets, such
as East Asia and East Europe, the former Soviet Union. For example,
due to the currency, financial and economic crises in most East
Asian countries, their economies are expected to have lower GDP
growth rates than before.
[0077] Simulating Low Demand Growth (2000LGR):
[0078] The 2000LGR scenario (LGR stands for "Low-Growth") is the
counterpart of 2000GR with new projections on demand and supply
shifts (again, the year may vary with the particular analysis
conducted). The scenarios with the adjusted demand/supply shifts
are referred to as the Low-Growth because the major differences
between 2000LGR and 2000GR result from the lower GDP growth (thus
the demand growth) in East Asia. It should be emphasized that the
"Low-Growth" scenarios are not sensitivity analyses, but rather as
more realistic projections of the year 2000 situation.
[0079] In 2000LGR, trade and domestic policies remain the same as
in the original BASE. Using 2000LGR as the new, Low-Growth BASE the
impacts of the GATT and domestic policy changes on world and
regional dairy markets are reassessed, and compared to the results
with those from the previous ceteris paribus analyses.
[0080] Forecasting 2000 with Adjusted Demand/Supply Shifters:
[0081] With the above adjustments to the demand/supply shifters,
two scenarios (2000LGRG and 2000LGRP) are simulated to forecast the
global dairy situation in the year 2000. Only trade policy changes
have been taken into account in the 2000LGRG simulation. Both trade
policy and domestic policy changes are considered in the 2000LGRP
simulation. 2000LGRG parallels the 2000GRG scenario but with
adjusted demand/supply shifts and 2000LGRP parallels 2000GRP in the
same fashion. Free Market at 2000 Revised (2000LGRFM): The
2000LGRFM Scenario reflects the full trade liberalization situation
in the year 2000 with the adjusted projections for demand/supply
changes.
[0082] Welfare Measures and General Results:
[0083] In addition to the traditional partial equilibrium welfare
measures, producer and consumer surplus, government revenues from
or expenditures on trade policies (tariff revenue minus export
subsidy spending, which can be considered as the net benefit to
taxpayers) as a part of total welfare are also considered.
[0084] In the GATT 2000/Domestic Policy Changes simulation the
focus is on the regional welfare implications of the changes in
trade/domestic policies in the scenarios relative to the
"Low-Growth" BASE (2000LGR). In the Free Market scenario the
welfare changes under full trade liberalization (versus the
"Low-Growth" BASE) are analyzed. In this Free Market scenario
(2000LGRFM), all tariffs and export subsidies are eliminated. Thus,
government revenues from and expenditures on trade policies are
zero.
[0085] Step I: Creating a Database of World Dairy Sector Data
100
[0086] A tremendous amount of data is required to operationalize
the world dairy hedonic spatial equilibrium model of the present
invention. As a result, the first step in the process of
forecasting the effects of trade policies on world dairy sector
attributes is to condition a preliminary set of data (for a number
of years) for use as the input to the BASE scenario model. This is
done by (a) compiling and updating a database of world dairy sector
data from various sources 110, (b) manipulating and transforming
the data to produce files of the data in a form usable by the
spatial equilibrium model of the present invention 120, and (c)
updating the BASE model files of aggregated data 130. It should be
noted, that it may be possible to acquire a preexisting database to
use as input to the model of the present invention, in which case,
this first step of creation of a database may be skipped.
[0087] Some of the main data inputs used to operate the BASE model
include (1) base year farm level prices and production of primary
commodities, wholesale level prices, production, and consumption of
secondary dairy products; (2) a regional wholesale sector
value-added matrix (farm wholesale processing and distribution
costs); (3) interregional transportation costs; (4) regional supply
and demand elasticities; (5) regional income elasticities; (6) GDP
growth rates; and (7) regional trade distortions. These data of
inputs are in some cases available as is, and in others must be
derived or calculated separately.
[0088] Compiling and Updating a Database of World Dairy Sector Data
110.
[0089] Much of the information on dairy production, consumption and
trade that is needed to perform the method of the present invention
is available in raw form from public sources. It should be noted,
however, that private sources of information may also be used to,
in some cases, more accurately simulate the effects of various
policy scenarios and supply and demand trends on the world dairy
sector.
[0090] Publicly available data used in the spatial equilibrium
world dairy model originates generally from three main sources, the
Food and Agriculture Organization of the United Nations (FAO), the
International Monetary Fund (IMF) and the Organization for Economic
Cooperation and Development (OECD).
[0091] In general, production and trade data for various years come
from the FAO and OECD (e.g., milk production by country, production
data of processed foods, and trade data by country; using OECD data
for all OECD countries). The exchange rate and gross domestic
product (GDP) growth rate data used in the model come from the IMF.
The price data and stock change data for the model is provided by
the OECD (OECD data is used for all countries where possible,
otherwise FAO data is used for the country). Regional trade
distortion data (regional export subsidies, import tariffs and
quotas, etc.) are obtained from the URA of the GATT. For certain
non-GATT member countries, the U.S. Dairy Export Council provides
tariff and import quota information. As well, other commercial
sources of actual (de facto versus dejure) implementation of the
URA GATT commitments can be utilized (e.g., Tariffic database).
[0092] Once the raw data is downloaded from the various sources,
the data must be cleaned (the labels of the data set are changed to
conform to the corresponding data labels in the relational
database, e.g. MS-Access) and resaved in a form importable into the
Access database.
[0093] The data is organized into raw data tables and grouping
tables. Raw data tables are tables that include one or more fields
that can be mathematically manipulated. Raw data tables are used to
store disaggregate raw data, e.g., by country and product. Raw data
tables include those for production (milk and commodity),
composition (milk and component), import quantity, import value,
export quantity, export value, price, stock, exchange rate and GDP
growth.
[0094] By contrast, grouping tables store information to define
aggregation and sorting criteria for a specific field. Grouping
tables include, e.g., region, product category, continent, region
order, and category order. By changing the information in these
tables, users may easily regroup or sort data in alternative
formats, making the data retrieval very flexible.
[0095] Countries are grouped into regions: 220 countries are
grouped into 21 regions including, WEU--all countries in Western
Europe, including Malta, EEU--all countries in Eastern Europe,
FSU--all countries from the former Soviet Union, CHN--China, Hong
Kong, Taiwan, Macao, and Mongolia, JAP--Japan, KOR--Korea, South
and North, SEA--Southeast Asia Countries to the east of Myanmar,
including Myanmar, IND--India, OSA--other South Asian counties,
AUS--Australia, NZL--New Zealand, MDE--Middle East including
Cyprus, NAF--North Africa, SAF--Republic of South Africa,
CAN--Canada, USA--U.S.A., MEX--Mexico, SAMN--South America
excluding Argentina, Chile, and Uruguay, SAMS--Argentina, Chile and
Uruguay, CAM--Central America and Caribbean Countries, excluding
Mexico, and ROW--all other countries, mostly in Sub-Sahara.
[0096] Product categories include MILK--milk of cows, buffalos,
goats, sheep, and camels; CHE--all types of cheese & curd
including fresh cheeses, such as cottage cheese; BUT--all milkfat
products, consisting of butter, ghee, and butter oil; WMP--whole
milk powders; SMP skim milk powders and buttermilk powders;
DWH--dry wheys; CAS--caseins and caseinates; CEM--condensed and
evaporated milks; and RES--dairy not included above, mainly fluid
milk, soft, and frozen products.
[0097] Regions are organized into continents as follows: W.
Europe=WEU; E. Europe/FSU=EEU, FSU; E. Asia=CHN, JAP, KOR, SEA; S.
Asia=IND, OSA; MidEast/NAF=MDE, NAF; N. America=CAN, USA; S/C
America=MEX, SAMN, SAMS, CAM; Oceania=AUS, NZL; and Rest of
World=SAF, ROW.
[0098] In summary, the database (updated with new data as and when
it becomes available) contains demand and supply data for 37 dairy
products and 220 countries. For production data, it has annual
production data for milk from 5 animal species, 7 types of cheese
(according to the milk origin), 5 types of milkfat products
(according to the milk origin), 6 dry dairy products (including
milk powder, casein and dry whey), and 4 kinds of condensed and
evaporated milk. There are no production data for fluid milk, soft
products, frozen products, and whey products except for dry whey.
These products are defined in the residual category. The unit for
production data is metric tons (MT).
[0099] Trade data include those for all products except that fresh
milk trade is treated in residual category rather than in raw milk.
This means that, "Fresh whole cow milk" is different from "Cow
milk", and "Fresh sheep milk" is not in the same category as "Sheep
milk". As a consequence, there are no trade data for raw milk.
There are 4 sets of trade data: Import quantity, Import value,
Export quantity, and Export value. The quantity data are in MT and
value data are in 1000 US dollars.
[0100] Price data are available only for 5 types of raw milk and
are in local currency units per MT. Very limited price information
for other dairy products from non-FAO sources has been added into
the database. The database also includes official exchange rate
data that are used to convert price data from local currencies into
U.S. dollars.
[0101] Stock data are available in aggregated form. For example,
rather than data for different types of cheese, only ending stock
data for cheese as a whole is available. There are five product
categories having stock data: cheese, butter, whole milk powder,
skim milk powder, and casein. If unavailable from the FAO, data are
gleaned from other sources to make the database as complete as
possible. Many sources provide annual stock change data rather than
ending stocks for each country. We convert stock change data into
ending stock by arbitrarily adding starting stock data for the
first year. Since in the majority of studies only stock changes are
of interest, this "conversion" should not affect data accuracy.
[0102] To estimate the trends in demand and supply changes the
database also includes real GDP growth rate data. Real GDP growth
includes both the population growth and GDP per capita change, and
has been adjusted for inflation. Data are obtained from the World
Bank, and are in percentage growth terms.
[0103] Trade policy and milk component data are not stored in
Access because they are in rather aggregated forms and involve many
calculations. These data are stored in a variety of Excel files
instead.
[0104] Manipulating and Transforming the Data to Produce Updated
Files in a Format Usable by the Model 120.
[0105] The data in the compiled database is manipulated to provide
information in a form appropriate for use in the model. Country
level data need tremendous data manipulation and processing to
obtain regional level computer input data. The compiled database
tables are queried to retrieve information of whatever sort is
needed by the model, and/or further calculations are made to derive
new information from the data. In this way, regional level data and
other calculated data are prepared for input to the BASE model.
[0106] Queries may be constructed to retrieve information for
regional milk production, milk price and milk composition, for
example. Standardization and/or reconstitution parameters may also
be derived. For example, the degree of intermediate dairy products
(skim and whole milk powder, evaporated/condensed milks, dry whey
protein concentrates, butter/anhydrous milk fat) and usage to make
the final demand dairy products (cheese and residual category
products such as fluid milk, frozen and soft products) may be
calculated by country and/or region. Any number of additional
queries are possible limited only by the imagination and
requirements of the user. The results of the queries may also be
exported in spreadsheet format, if desired.
[0107] Various calculations are also performed to determine other
values for use by the model. For example, consumption is generally
computed from a supply and demand balance worksheet where
consumption is taken as the residual of
Production+Imports-Exports+Beginning Stocks-Ending Stocks
(=Consumption; if stocks data are missing, they are omitted in
consumption). Another calculation is performed to increase the
accuracy of FAO data on production and prices. A three-year average
is calculated for any given year's data (e.g. 1995-1997 data
averaged to give year 1996 value). In this way the more recent year
data of the older database are updated using current year data.
Interregional transportation costs (TC) are calculated as flat
transportation costs (e.g., for non-refrigerated products (whole
and skim milk powder, casein, evaporated & condensed milk and
dry whey), TC=$0.018/MT/Nautical mile; for refrigerated products
(cheese and butter), TC=$0.027/MT/Nautical mile; and a very high
rate is used for fresh milk products (to characterize partially
high trade barriers on fresh milk products)). As well, commercial
sources can be used to obtain more detailed and country to country
specific transportation costs. Distance data are derived from
Defense Mapping Agency data.
[0108] Updating Supply/Demand Trends and Exchange Rates 125.
[0109] Naive supply and demand trends are updated by choosing
compound growth rates (by product and by country) to minimize
forecast error over the 5 years prior to and including the current
BASE year data. Annual quantity forecasts are generated from BASE
data using compound growth rates for each product and region.
Prices are adjusted to quantity forecasts by subtracting price
changes/demand (supply) elasticity from the forecast demand
(supply) changes. The GDP and population projections are used with
income elasticities to forecast demand for product/region.
[0110] The BASE model is run to generate linear regional supply and
demand curves using regional supply and demand elasticities
(derived from USDA SWOPSIM data; see, Roningen, V., J. Sullivan,
and P. Dixit, 1991, Documentation of the Static World Policy
Simulation (SWOPSIM) Modeling Framework, Staff Report No. AGES
9151, Washington, D.C.: USDA/ERS) and base level prices and
quantities. Regional income elasticity data are derived from USDA
SWOPSIM for major countries, and is computed for other countries
assuming that countries having similar development status have
similar demand characteristics.
[0111] Updating the BASE Model Aggregated Data Files 130.
[0112] Newly retrieved, manipulated and in some cases updated data
(as described above) are merged with current BASE model files to
update them. Once this is done, the model itself can be updated as
per Step II below.
[0113] Updated data include (a) regional milk production, price and
composition; (b) regional production, consumption, stocks,
imports/exports and price for all commodities; and, (c) component
balance at the regional level (milkfat, casein, whey protein,
lactose). The updated component balance includes (a) production of
milk components (using FAO data); (b) utilization of milk
components (using FAO data); and, balance of the surplus/shortage
on the residual (nontraded) product category.
[0114] The result of Step I is to transform the model's files of
world dairy sector information to accurately reflect the recent
world economic conditions and to be usable by the model. In this
way, the BASE scenario model is specified to provide an accurate
representation of the world dairy markets and reflects recent world
economic conditions.
[0115] Step II: Refining the Model 200
[0116] During this step of the method, the BASE model data are
adjusted to be consistent with model specifications before the
model is used to do other analyses: (a) the BASE model of the world
dairy sector is run to generate preliminary world dairy sector
attribute forecasts 210, (b) prices are calibrated and the model
resolved with the price calibrations and updated ad-valorem tariff
rates 220, and (c) the results are validated and the model
parameters refined accordingly 230-250. This process is iterative
and results in a refined model able to predict world dairy sector
attributes accurately. Solving the resulting refined BASE model
yields optimal regional values for milk/commodity production and
consumption, commodity trade flows, milk/commodity prices and
implicit component prices (fat, casein, whey protein and lactose).
These resulting BASE values can then be used to measure changes
that result when the model is resolved under various policy
scenarios in order to determine their effects on the world dairy
sector (see Step III).
[0117] Run BASE Model of the World Dairy Sector to Generate
Preliminary World Dairy Sector Attribute Forecasts 210.
[0118] Using the updated model files, the BASE model is run to
generate a preliminary set of annualized forecasts. Output summary
files are created for farm level prices and production; commodity
prices, production and consumption by product and country/region;
imports and exports by product and by country/region; commodity
trade flows by product and by country/region; and producer and
consumer surplus (welfare), net costs to treasury (tariff revenues
minus export subsidy and intervention price expenditures).
[0119] Calibrate Prices and Resolve the Model with the Price
Calibrations qnd Updated Ad-Valorem Tariffrates 220.
[0120] Price calibrations are performed in order to address certain
limitations of the data. FAO provides price data only for primary
products (raw milk prices). The secondary dairy product price data
is obtained from several other sources that, unfortunately, only
provide information for major dairy countries and major dairy
products. Moreover, very limited information is available on dairy
manufacturing and distribution costs. Estimates are made of the
manufacturing and distribution costs for major dairy products
(cheddar cheese, butter, skim milk powder, and whole milk powder)
in several countries (mostly OECD countries). To handle these data
limitations, the model is used to compute unknown manufacturing and
other cost parameters while solving for the optimal base
solution.
[0121] The basic idea of this calibration procedure is to search
for the values for those unknowns that are consistent with the
model specifications, equilibrium conditions and the parameters
based on data that are available. This involves solving the model a
number of times with the calibrated data updated in each run. The
procedure can be divided into the following steps. Step one:
"guess" the values of the unknown manufacturing and other cost
parameters as the starting values and solve the model. Step two:
compare the model solutions with the data, which include the
original "guessed" data. Adjust those "guessed" data/parameters in
the direction that will potentially reduce the deviation of model
solutions from the data, and solve the model again. Step three:
repeat step two until no further significant changes are needed to
alter the model solution.
[0122] The goal of calibration via updating manufacturing costs is
to replicate the data for regional milk price and production data
by choosing region-specific adjustments on processing costs. Using
the procedure described above we obtain region-specific price
calibration wedges that make the regional milk prices in the model
solution the same as observed price data.
[0123] Given that the milk supply curves are fixed, calibrating the
milk price in this manner is equivalent to calibrating regional
milk production because the calibration procedure is to move the
equilibrium points along the fixed supply curves. As for the
calibration of regional prices of secondary products, the position
of the associated regional demand curves is adjusted to the points
that are relatively consistent with milk supply curves and other
demand curves, on which good information (generally the regions
including OECD countries) is available. Using the procedure
described above, the unknown prices, thus regional consumption, can
be calibrated. The regional demand curves are then reset with the
updated prices by re-computing prices intercepts and slopes under
standard formulas using assumed demand elasticities, BASE quantity
and calibrated price data. After sufficient iteration of the
calibration process, BASE data is replaced with the current model
solutions for all non-OECD prices.
[0124] Market prices are treated as endogenous in the calculation
of tariffs. This is done by solving for market equilibrium
iteratively, where each iteration uses updated specific duties
equivalent of the ad valorem tariffs, until convergence is
obtained. Upon convergence, the solution is identical to the one
obtained from solving directly the associated mixed complementarity
problem. Finally, most non-tariffs barriers influence import volume
directly and can be introduced easily in spatial trade models by
adding appropriate restrictions on quantities traded.
[0125] Validate Results and Refine the Model Parameters Accordingly
230-250.
[0126] Once price calibrations are complete, the model is resolved
with the calibrated price data and updated endogenous ad-valorem
tariff rates 230. The model solutions are validated by comparing
them with actual data 240 and the model parameters refined
accordingly 250 to better align the model results with the actual
data.
[0127] Some of the model parameters refined by the process include
(a) domestic policy parameters (e.g. intervention prices,
production/consumption subsidies, quota rents, fluid/manufacturing
milk price wedges), (b) trade policy parameters (e.g. GATT
commitments (import quotas, two-tiered import tariffs (within and
over quota), export subsidies (quantity and expenditure)), and (c)
standardization/reconstitu- tion parameters (e.g., the degree of
intermediate dairy products usage (skim and whole milk powder,
evaporated/condensed milks, dry whey protein concentrates,
butter/anhydrous milk fat) to make final demand dairy products
(e.g., cheese and residual category (fluid milk, frozen and soft
products) by country/region).
[0128] As an example of the validation procedure, consider the
following. The BASE model is run to forecast annually to 2000 using
only information available in 1995. Native supply/demand shifters
based on 1989-1994 data and annual exchange rate forecasts are
employed. The resulting annual forecasts are then compared with
actual annual data from 1996, 1997, 1998, and 1999 for farm prices,
milk and commodity production, trade, etc. The accuracy of the
model can then be assessed and the model assumptions (e.g.,
supply/demand trends) refined accordingly. The focus is on
near-term assumptions as these will affect the accuracy of the
shorter-term forecasts.
[0129] The model is run again with the refined parameters and the
validation process repeated until the model solutions conform
acceptably to the actual data. When this occurs, the model is
deemed to be refined sufficiently for its forecasts to be used for
comparison with model results under various policy scenarios. The
refined BASE model 310 yields forecasted optimal regional
milk/commodity production and consumption, commodity trade flow,
milk/commodity prices and implicit component prices (fat, casein,
whey protein and lactose), among other forecasted world dairy
sector attributes.
[0130] The validated model is run to forecast out 5 years, updating
the next year forecast with the current model solution (see, e.g.,
FIG. 3 sample output table, also including validations). Thus the
model produces five years worth of annual forecasts that can be
updated periodically as new data are acquired.
[0131] Step III: Running the Updated Model Under a Plurality of
Scenarios to Forecast the Effects of Each of the Scenarios on the
World Dairy Sector Attributes 300.
[0132] The BASE simulation described in the previous section,
provides a reasonably good representation of world dairy markets.
For that reason, it may be used as a benchmark to compare results
from other simulations 340. The model is modified to reflect
various policy scenarios and run to generate world dairy sector
attributes under each of the policy scenarios 320, and these
forecast results 330 are then compared with those of the BASE run
in order to determine the effects of each of the policies on the
world dairy sector 340.
[0133] Run Model Under Various Policy Scenarios 320.
[0134] The policy parameters of the BASE model are adjusted
according to each policy scenario and the model solved (examples of
several domestic and trade policy scenarios are given above in the
section on the model and policy scenarios). The model is run to
simulate the effects of a policy and generates annualized (and
optionally also longer-term) forecasts of various attributes of the
world dairy sector including supply and demand trends and exchange
rate changes.
[0135] Compare Forecast Results with those of the BASE Run to
Determine Policy Effects 340.
[0136] Output files are generated from each policy scenario 330 run
and compared with the BASE solutions 340 in order to solve for the
effects of the policy scenario. Sample output tables are given in
FIGS. 4 and 5, by way of example of the effects of various policy
scenarios on the world dairy sector attributes of farm milk prices
and maximum allowable subsidied exports (note that the output may
be summarized in a variety of ways besides in table format,
including graphs and the like). Other attributes may be likewise
summarized. Please note that though FIG. 1 depicts the forecasting
of three policy scenarios at 330, any number of scenarios may be
run.
[0137] Other Embodiments
[0138] While the above description contains many specificities,
these should not be construed as limitations on the scope of the
invention, but rather as an exemplification of various embodiments
thereof. The above-described embodiments are set forth by way of
example and are not for the purpose of limiting the present
invention. It will be readily apparent to those skilled in the art
that obvious modifications, derivations and variations can be made
without departing from the scope of the invention. For example,
[0139] a) the database of the present invention may include private
sources of information in addition to the publicly available
sources;
[0140] b) other public sources of data may be used in addition to
those described above;
[0141] c) regions may be formed by aggregating countries
differently than described herein;
[0142] d) dairy components may be aggregated in different ways to
the various categories of commodities; and,
[0143] e) the parameters of the model may be modified to reflect a
variety of policy, as well as non-policy scenarios.
[0144] Accordingly, the scope of the invention should be determined
not by the examples given, but by the appended claims and their
legal equivalents.
* * * * *