Sharples, Jerry A.

English

Research Papers in Economics

Agriculture  and Trade Analysis Division
Economic Research  Service
United States Department of Agriculture
Staff Report # AGES870928
1988
Trade  and  Development
Shane,  M., ed.
Proceedings of a Meeting  of the
International Agricultural  Trade  Research  Consortium
December,  1986,  CIMMYT,  Mexico City, Mexico
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searc  ConsrtkE ~CitvMexBUILDING  A  WORLD  TRADE  MODEL:  SOME  LESSONS
WE  ARE  LEARNING
Jerry Sharples1
The Agricultural  and Trade  Policy  Branch (ATP)  of the  Agriculture  and  Trade  Analysis
Division,  ERS,  has  been  in  the trade-modeling  business  for  many years,  working  with  large
dynamic  multicommodity  models like  GOL  and  the IIASA  model,  and  small  single-commodity
models.  We  are  currently  building  another world  trade  model  as  a  part  of a  broader  ERS
study  of trade  liberalization.  During  our past and  present  modeling  activities,  we  have  learned
some  lessons.  This  morning,  I  will  discuss some  of those  lessons  as  well  as  some  present
model-related  problems.
The Optimal  Trade  Model
In  our trade  modeling  work,  we  are constantly  reminded  of a concept  called  the  "optimal  trade
model."  One  could  think of this  concept  as  a  math ematical  function:
Trade  model  =  f  ( S,  I,  C,  D,  O )
with  the  arguments  in the  function  being:
S  =  the problem  setting
I  =  inputs to  the  modeling  process (people  and  budget)
C =  computer  technology
D =  data (prices,  quantities,  elasticities,  etc.),  and
O =  other factors  (omitted from  this  discussion).
The  modeler  wishes  to maximize  this function (that  is,  find  the  optimal  trade  model),  given
the current  values  of  the independent  variables.  I'll  discuss  how the  first four  arguments  of
this function  are  shaping  our current trade-modeling  efforts.
The  Problem  Setting
The setting  relates  to  the  nature  of  the research  problem  and  the  nature  of the  research
institution.  Both  have  an  impact  on  what  constitutes  an  optimal  trade  model.  As  a  part  of
its  current trade  liberalization  study,  ERS  needs  a  world  trade  model  for  evaluating  changes  in
various  countries'  agricultural  policies--domestic  policies  and  trade  policies.  This  research
problem  dictates  that the  model  should  include  such  characteristics  as  multiple  countries,
multiple  commodities,  production,  domestic  consumption,  trade,  policies,  etc.
Any  model  of significant  size  in  ERS  should be  designed  to  have  a useful  life  well  beyond  the
immediate  research  needs.  An agricultural  trade  model  is  no  exception.  We  anticipate
recurring  trade  policy  and  related  issues  that  could  be  addressed  with  a trade  model.  To  help
ensure that  a model  will  continue  to  be  used  in the  institutional setting  of ERS  two  additional
model  characteristics  are desirable:  quick turn-around  and transferability.  Quick turn-around
is needed  because  of  the  short  fuse on  many  requests  for research  that come  from  outside  the
agency.  The  model  must  be  easy  to  modify,  quick  to  solve,  and give  results  that are  easy  to
interpret,  or else  it will  have  very  limited  use  in  ERS.  The  model  must  also  be relatively  easy
to  transfer  among  researchers  so  that it will  not die if one  or two  key  researchers  leave  the
project.  The  conceptually  simpler,  well-documented  model  has  a better chance  of  surviving
and being transferred  among  researchers.
1The author  is an economist,  Agriculture and Trade Analysis  Division,  Economic  Research  Service,  U.S.  Department
of Agriculture.
154Inputs  to  the Modeling  Process
The optimal  trade  model  is also  influenced  by the  available  research  budget  and  professional
staff.  A larger  modeling effort  can  be  pursued  with  more  inputs.  The  equivalent  of about  3
scientist-years  (spread  over  about eight  people)  are  being  invested  directly  in model
construction  and  use.  Many  more  ERS researchers  with country/region  expertise  are  indirectly
involved  in constructing  policy  variables  and  evaluating  model  parameters.
Computer Technology
The trade  model  is  being  designed  for the  personal  computer.  The personal  computer  has
provided  a major  breakthrough  for policy  modelers  in ERS.  It  enables  the analyst  to  obtain
quick  turn-around  from sophisticated  models  at low  cost.  And  the  software  allows  excellent
interaction  between  the  model and  the modeler.  These  characteristics  greatly  increase  the
potential  for  using  formal  models  for  policy  analysis.
Data
The  quality  of the  available  data  and coefficients  help  determine  the nature  of a  model.  The
model  is  no  better  than the  data  in it.  If  the  quality of  the available  data  is  poor,  then
there  is  little justification  for  building  a complicated  model.  A  trade  policy  model needs  data
for  quantities (supplied,  demanded,  traded),  prices,  technical  coefficients,  elasticities,  and
policy  variables  for major  traded  commodities  and  countries.  The  quality  of  the data  on
quantities  is  relatively  good,  but  the quality  of the  rest  is quite  poor.  This  suggests  that a
trade  model  should  be  kept  relatively  simple.
Given  the  current  status  of S,  I,  C,  and  D  in the  ATP  Branch,  we  have defined  what  we  think
is  an  optimal  trade  model.  To  be  more  accurate,  a  modeling  process  is  being  followed  that
will  yield  one  rather large  trade  model  as  well  as  many  smaller  trade  models.  It differs  from
past  trade  modeling  activities  in ERS  because  (a)  we  have  learned  from  the  past,  and  (b)  there
have  been  significant  changes  in several  of the  arguments  in  the  trade  model  function,
especially  the introduction  of the  microcomputer.
I'll discuss  two  components  of our current  trade model:  the  structure  of the  model  and  the
algorithm  used  to  solve  the model.  We  call  the model  "TLIB" for  trade  liberalization  and  we
call  the  algorithm  "SWOPSIM"  for Static  World  Policy  Simulation.
Structure of the  Tlib  Model
TLIB  is  a static,  equilibrium,  net  trade,  synthetic  model  of  world  agricultural  trade  in  1984.
The complete  model contains  22  commodities  and  36  countries.  As  described  below,  world
models  with  fewer  commodities  and  countries  can  be  constructed  from  the  complete  model.
We  assume  that the  base  year  (1984)  is  in  intermediate-run  (3-  to  5-year)  static  equilibrium.
In  other words,  the  model  is  designed  so  that  the  "base solution"  reproduces  the  base  year
prices  and quantities  produced,  consumed,  and  traded.  Thus,  this  base  solution  represents  the
outcome  as  a result of agricultural  policies  that were  in place  in  all countries  in  1984.  All of
the  model's price  elasticities  also  represent  a  3-  to  5-year  adjustment  period.
One  does  policy analysis  by  changing  selected  policy  variables in  the  base  model  and obtaining
a new solution.  The  difference  between  the  new and  base  static  equilibrium  solutions  suggests
how the  policy  change  would  have  affected  the  world,  as  it existed  in  1984,  but after a  3-  to
5-year  period of adjustment.
155TLIB  is a synthetic  model,  meaning  that the builders  obtained  the  estimates  of parameters
from  secondary  sources  rather  than  by using  econometric  techniques.  The  theory  of
competitive  production,  consumption,  and  trade  of homogeneous  goods  is  used  to  specify
relationships  among  the parameters,  and  to  reduce  the  number  of estimates  needed.
Why  static?  A static  model  is simpler  than  a  dynamic  model.  The  additional  work  of adding
dynamics  to the  model  does  not appear to  be  cost  effective  at this  time.
Why  build a n.t trade  model  rather  than a model  that incorporates  specific  trade  flows  among
trading  countries,  such  as  an Armington  model  or transportation  model?  The  main  reason  is
that  the  net trade  model is  simpler  and  requires  less data.  Besides,  the  main  research  interest
of the  trade  liberalization  study  is not on individual  trade  flows  among  countries.  We  are,
however,  experimenting  with an  Armington  version.2
The  TLIB  model  is  conceptually  quite  simple,  yet it  provides  powerful  conclusions.  It  provides
estimates  of net changes  in production,  consumption,  prices,  and  shifts  in  welfare  across
sectors  and  countries  for a  wide variety  of policy  changes  or other  shocks  to  the  base  model.
These  changes,  however,  have  to  be  interpreted  within  the  context of  the model's  assumptions
about  how  the  world  works.  More  is  said  in  the last  section  about  how  one  tests  the  validity
of  this model  in representing  the  real world.
The  Algorithm  - SWOPSIM
The  algorithm  used  to  build and compute  solutions  to  the TLIB  model  is  called  SWOPSIM.3  Its
creator  is Vernon  Roningen.  SWOPSIM  uses  the  latest spreadsheet  software  and  can  be
handled  by  the  more  powerful  microcomputers.  The spreadsheet  approach  meets  several  of  our
objectives  quite  well.  Its a  widely  used  and  flexible  technology  that  simplifies  the  problem  of
transferring  the  model  to  other users.  It allows  interaction  between  the  user  and  the  model.
And  with  the right  match  of model  size  and  computer  capability,  the  spreadsheet  provides
quick  turn-around.
SWOPSIM  stores data,  builds  trade  models,  solves  trade  models,  and  reports results.  At
present,  the  data  set represents  22  commodities  for 36  regions.  The  "model"  discussed  here
refers  to  that  total  data  set.  From that  common  data  set,  however,  SWOPSIM  can  create
single-commodity  world  trade  models  or  multicommodity  models.  It  can  create  two-region
world  models  or N-region  world  models  with  one  region  always  being  "the  rest  of the  world."
That  flexibility  allows  the  model  to  be  tailored  to  the  specific  problem  being  investigated.
Modeling  Problems
There  are  numerous  problems  associated  with  building  a trade  model.  I'll mention  five  major
problems  that we  had  to  address.
1. Base  Period.  The  TLIB  model  is  assumed  to  be  in  an intermediate run equilibrium in the
base  period.  Thus  it is  important that world  agriculture  roughly  approximate  an intermediate
run equilibrium during  the  base  period.  The  base  period might be  defined  as  the  average  of
2  Praveen M.  Dixit  and Vernon  Oley Roningen.  Modeling  Bilateral Trade Flows  with the Static World Policy
Simulation  (SWOPSIM)  Modeling  Framework.  ERS Staff Report  No.  AGES861124.,  Econ.  Res.  Serv.,  U.S.  Dept. Agr.,  Dec.
3  Vernon Oley  Roningen.  A Static World Policy  Simulation  (SWOPSIM)  Modeling  Framework.  ERS  Staff Report No.
AGES860625.,  Econ.  Res.  Serv.,  U.S.  Dept.  Agr., July 1986.
156several  years  or  as  one  year.  An  average  smooths  out  year-to-year  random  variations  in
prices  and quantities.  On  the other  hand,  it  is  easier  to define  policies  for  a  specific  year.
We  chose to define  a  1-year  (1984)  base  period.
2.  Time  Horizon.  Should  the model  be  short  run, long  run,  or  something  between?  Since
"trade  liberalization"  is the  research  issue,  we  discarded  the  short  run  and  assumed  that  the
model  represented  an  equilibrium  solution  that  would  result  after  a  3-  to  5-year  period  of
adjustment.
3.  Obtaining Price  Elasticities.  The full  TLIB  model  needs  about  3,600  own-  and  cross-price
elasticities.  It  is impossible  to  obtain  a consistent  set  of econometrically  estimated  values  for
all.  In  addition,  the  data do  not exist  for  many countries.  As  a  substitute,  results  of other
studies  are  used  and  experts  are  polled.  Further,  theory  provides  opportunities  to  make
simplifying  assumptions.  For  example,  first order  conditions  from  a  system  of competitive
supply  equations  can  be used  to specify  certain  cross-price  elasticities.
4.  How  Insert Policy?  There  are  three  ways  to  insert  policy  into  the  TLIB  model:  (a)  with
price wedges  between  the  world price  and  domestic  producer  and  consumer  prices  (for
example,  producer  and consumer  subsidy  equivalents),  (b)  by  altering  domestic  supply  and
demand equations,  and  (c)  with price  transmission  elasticities.  We  plan  to  use  all  three.  We
assume  that when  a country  fully liberalizes  trade,  changes  in  world  prices  are  fully
transmitted  to domestic  producers  and consumers.  For  these  scenarios,  price  transmission
elasticities  are  set at  1.0.
5. How  Test the  Model?  How  does  one  evaluate  a  static  synthetic  model?  I know  of  no
objective  tests.  By  definition,  the model  will  reproduce  the  base  period.  Alternative  solutions
are obtained  by changing  one  or more  parameters  in the  model.  Those  solutions  represent
outcomes  that are  not experienced  by  the  real world  in  the  base  period.  I  conclude  that  an
evaluation  of the  model  must be  based  upon extensive  sensitivity  analysis  and  the  analyst's
judgment  on  the reasonableness  of the  results.
We  use  several  sensitivity  tests.  For  example,  we  estimate  the  implied  elasticity  of demand
for U.S.  exports  by shifting  the  U.S. supply  functions.  Results  show  new  export  prices  and
new  export quantities  that can  be  used,  with  base  solution  export  prices  and  quantities,  to
calculate export  demand  elasticities.  Those  export  demand  elasticities  are  functions  of
quantities  and  elasticities  in  all  other  countries.  Thus,  their  interaction  can  be  captured  in
one  summary  statistic  that  has  some  meaning  to  the  analyst.
The model-building  process  described  in  this  paper  is  summed  up  in  one  sentence  in  the  text
by  Pindyck  and  Rubinfeld,  "Model  building  is  very  much  an  art,  and  part  of  that  art  is
learning  to  trade off alternative  criteria  in  different  ways.4
4 Robert S.  Pindyck  and Daniel  L. Rubinfeld.  Econometric Models  and Economic  Forecasts.  Second  Edition,
McGraw-Hill  Book  Co.,  New  York,  1981,  p.  367.
157

Building a World Trade Model: Some Lessons We Are Learning

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