Model
Overview
Chapter 8 uses an industry-specific partial equilibrium
model of the global rice market to simulate the effects of country-specific
policies on the industry and consumers in the United States and other major rice-producing
and -consuming countries. The prospective model is calibrated to 2023 trade
data with two types of rice (aromatic and nonaromatic), two stages of
processing (paddy and milled rice), and 185 countries aggregated into 18
country groups (16 individual countries and two aggregate groups). The model is
used to analyze the impacts of tariffs, export bans, tariff-rate quotas (TRQs),
export charges, minimum support prices, and government-to-government contracts.
Table E.1 List of countries included in the rice
model
Group
|
Countries
|
Individual countries in the
model
|
Bangladesh, Brazil, Burma,
Cambodia, China, India, Indonesia, Japan, Pakistan, Paraguay, Philippines,
South Korea, Thailand, United States, Uruguay, Vietnam
|
Aggregate
groups
|
Other
developed countries, other developing countries
|
Source: Compiled by the USITC.
Supply
Structure
In the following three sections, the subscripts ,
,
,
and denote the type of rice (aromatic or
nonaromatic), exporting country, importing country, and factor input in the
production of paddy rice, respectively.
The model assumes a perfectly competitive global rice market
where each country produces a unique variety of rice with characteristics that
differentiate it from rice produced in other countries. Four countries—India,
Pakistan, Thailand, and Vietnam—produce aromatic rice in addition to
nonaromatic rice and produce a unique variety of each type. The production
process consists of two steps: rice farming and milling (processing). Trade can
occur after either step in the production process. That is, rice can be traded
as paddy rice and then processed in the destination country or processed in the
source country and traded as milled rice. The supply of paddy rice in metric
tons ( ) from each country is determined by the
equation below.
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is the producer price of paddy rice type in country ;
are general ad valorem production subsidies
for rice type produced in country ;
and is the elasticity of supply for paddy rice.
The calibrated supply shift parameter ( ) captures country-specific factors for each
rice type that shift the supply curve.
In the model, eight factor inputs are used in paddy rice
farming: fertilizer, pesticides, energy, water, seed, land, labor, and capital.
The quantity of each factor input used to produce one unit of paddy rice
quantity ( ) in country is represented by .
The price of each unit of factor input in country used in the farming of rice type is .
The first equation below represents each factor input’s share of the total cost
of production of paddy rice, where and is the total number of factors used in the
production of paddy rice. The cost shares of factor input in the production of paddy rice type in country ( ) comes from the Cobb-Douglas production
function for paddy rice in the second equation below.
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In the model, the processing of paddy rice into milled rice
is represented by the Leontief production function with two inputs, paddy rice
( ) and all the value-added resources used in
the milling process, in the equation below.
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is the milling quantity recovery rate for rice
type in country ,
and the quantity of milled rice produced is .
is the total cost of milling, and is the fixed value added to paddy rice type of milling in country .
Demand Structure
The model has a nested constant elasticity of substitution
(CES) demand structure. Consumers choose the types and sources of rice among
which to allocate their total expenditure on rice. The consumers’ decision is
determined by maximizing their CES utility function subject to their budget
constraint. The aggregate utility function for country is represented by the following three
equations where and are the total number of source countries and
rice types, respectively.
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In the above equations, is the calibrated demand preference parameter
of consumers for the rice type imported by country from country and is the calibrated demand preference parameter
for country composite rice type in country .
is the elasticity of substitution between
sources of each rice type ,
and is the elasticity of substitution between
nonaromatic and aromatic types of rice. The budget constraint for country is that total expenditure ( ) must equal the quantity of rice supplied ( ) multiplied by the delivered price of rice ( ) type from source country to destination country .
The delivered prices are a function of the producer prices, policy-induced
price shifters, and location of paddy rice milling defined by the equation
below.
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All policy-induced price shifters are included as ad valorem
rates. is the tariff rate applied by country to imports from country of rice type ;
is the consumption subsidy in country for rice type ;
and are the export charges applied by country on its exports to country of rice type .
The quota rent rate ( ) is the cost of quota policies, including
export quotas and import quotas. The quota policies cause a discrepancy between
supply and demand that can result in higher prices. The quota rent rate behaves
like a tariff except, instead of the government receiving revenue, the
exporting producers restricted by the quota receive economic rents from higher
prices. The rent rate is endogenous in the model and is required for all
counterfactuals, including those that do not directly simulate changes to quota
policies. All counterfactuals shift the equilibrium and affect the size of the
discrepancy between supply and demand caused by the import and export quotas.
Outside of the policy-induced price shifters, delivered
prices also include differences depending on where the paddy rice is processed.
is the share of rice type exported from country to country that is paddy rice; and are the milling recovery rates of rice type for countries and ,
respectively; and and are the fixed milling costs of paddy rice in
countries and ,
respectively.
The demand function for rice type exported from country to country implied by country ’s utility function is defined below.
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The parameter is the price elasticity of total industry
demand, and the CES price index in the inner nest for the two different
composite rice types in country ( ) is represented by the following equation.
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The CES price index in the outer nest ( ), which represents the average consumer price
for all rice in country ,
is defined below.
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The global demand for each paddy rice type supplied by country is represented by the following equation,
where is the total number of importing countries.
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The market clearing condition is defined by the following
equation.
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Some countries in the model have import quotas or export
controls. In these cases, the bilateral trade flows that are subject to the
quotas must also satisfy the market clearing conditions in the equations below.
are the binding TRQ quantities applied by
country to imports of rice type from country ,
and are the quantitative export controls applied
by country to its exports of rice type to country .
In certain instances (e.g., India exporting to Japan), one country has an
export quota and the other has an import quota. In these cases, the model
assumes that the export control is binding.
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Data
Description
The model uses producer prices and bilateral trade in
quantities as the main inputs along with many parameters. The bilateral trade
data come from the Global Trade Analytics Suite (GTAS).
The 6-digit level of the Harmonized Commodity Description and Coding System
(HS) includes four categories of rice (table E.2).
Table E.2 Harmonized Commodity Description and
Coding System (HS) codes and descriptions for rice
HS 6-digit code
|
Description
|
100610
|
Rice in the husk (paddy or rough)
|
100620
|
Husked (brown) rice
|
100630
|
Semi-milled or wholly milled rice, whether or not
polished or glazed
|
100640
|
Broken rice
|
Source: S&P Global, GTAS database,
accessed August 30, 2024.
The parameter ,
which represents the share of total rice trade that is paddy rice (described by
the HS as rice in the husk), is calculated from the trade data by dividing the
quantity of trade classified under HS code 100610 in metric tons by the total
rice traded. Once the share parameter is determined, all rice trade is
aggregated into one HS 4-digit group (1006). Additionally, the model uses
production data in combination with the trade data to calculate domestic
shipments of each rice type ( ). The production data come from the U.S.
Department of Agriculture (USDA) Production, Supply and Distribution (PSD)
Online database.
Domestic shipments are calculated by subtracting each country’s exports from
its production.
Four countries examined for this report produce aromatic rice: India, Pakistan,
Thailand, and Vietnam. Neither the PSD Online production data nor the HS
classifications for trade data disaggregate aromatic and nonaromatic rice. To
disaggregate the data, the model uses share parameters from the RiceFlow model
used in the 2015 U.S. International Trade Commission (Commission) report on
rice.
These parameters provide the share of production in each country that is
aromatic and the share of each bilateral trade flow that is aromatic.
Where more recent data were available, these shares were updated using
country-specific information and sources.
The table below provides the aromatic share of production data that were used
in the model for this report.
Table E.3 Aromatic share of production data
Country
|
Aromatic share of production
|
Source
|
Year of data
|
India
|
0.096
|
India Grains and Feed Annual Report
|
2023
|
Pakistan
|
0.350
|
RiceFlow
|
2013
|
Thailand
|
0.350
|
S&P Export Data and the Government of Thailand
Office of Agricultural Economics
|
2023
|
Vietnam
|
0.20
|
United States Department of
Agriculture, United States government official
|
2023
|
Source: Durand-Morat and Wailes, RiceFlow,
accessed various dates; USDA, accessed various dates; Government of Thailand
Office of Agricultural Economics, accessed March 2024; interview with foreign
government official, accessed September 20, 2024; S&P Global, Total
exports, HS heading 1006, rice, accessed August 30, 2024.
The PSD Online production data provide the production of
both paddy and milled rice for each country. The milling quantity recover rates
( and ) are calculated by dividing each country’s
milled production by its paddy rice production.
Producer prices for nonaromatic paddy rice data come from
the Food and Agriculture Organization of the United Nations (FAO) FAOSTAT
database and data from the RiceFlow model.
The annual producer prices of paddy rice are only available for the 2022
calendar year, so the model proxies the 2023 producer price data with the 2022
data. The table provides the prices, source, and year of data.
Table
E.4 Producer price of rice data
In dollars; FAO = Food and Agriculture Organization; — (em
dash) = not applicable; mt = metric tons.
Trading partner
|
Nonaromatic producer price ($/mt)
|
Source nonaromatic
|
Year of nonaromatic data
|
Bangladesh
|
220.40
|
FAO/RiceFlow (imputed value)
|
—
|
Brazil
|
279.10
|
FAO
|
2022
|
Burma
|
179.22
|
FAO/RiceFlow (imputed value)
|
—
|
Cambodia
|
267.17
|
FAO/RiceFlow (imputed value)
|
—
|
China
|
413.10
|
FAO
|
2022
|
India
|
222.96
|
FAO/RiceFlow (imputed value)
|
—
|
Indonesia
|
392.39
|
FAO
|
2022
|
Japan
|
1413.20
|
FAO
|
2022
|
Pakistan
|
735.10
|
FAO
|
2022
|
Paraguay
|
354.00
|
FAO/RiceFlow (imputed value)
|
—
|
Philippines
|
320.10
|
FAO
|
2022
|
South Korea
|
1803.90
|
FAO
|
2022
|
Thailand
|
299.20
|
FAO
|
2022
|
United States
|
427.69
|
FAO
|
2022
|
Uruguay
|
235.00
|
FAO
|
2022
|
Vietnam
|
311.40
|
FAO
|
2022
|
Developed group
|
331.35
|
FAO (average of developed)
|
2022
|
Developing group
|
303.81
|
FAO (average of developing)
|
2022
|
Source: FAO, FAOSTAT database, “Producer
Price”, accessed various dates; USITC imputed value using data from FAO,
“Producer Price,” FAOSTAT database and Durand-Morat and Wailes, RiceFlow,
accessed various dates.
Not all individual countries have producer prices available
in the FAOSTAT database. For those without producer price data available,
producer prices from the 2015 Commission report are used and scaled up by the
average price increase from 2015 to 2022 for the countries where 2022 producer
price data are available. The producer price of aromatic rice is calculated by
scaling up the 2022 producer prices of nonaromatic rice from FAOSTAT, using the
relative difference between aromatic and nonaromatic rice prices in the 2015
Commission report that used the RiceFlow model.
The producer prices for the aggregated developing-country and developed-country
groups are the simple average of FAO producer prices in the developed and
developing countries.
On the supply side, the model uses the factor input shares
of total production costs ( ) as inputs. These shares are from various
country-specific sources, and where no cost-of-production data more recent than
2013 are available, the cost shares from the 2015 RiceFlow model are used.
The table below lists the countries, sources, and year of the cost shares used.
Table E.5 Sources and year of most recent cost shares
of production data
Trading partner
|
Source
|
Year
|
Bangladesh
|
RiceFlow
|
2013
|
Brazil
|
RiceFlow
|
2013
|
Burma
|
RiceFlow
|
2013
|
Cambodia
|
RiceFlow
|
2013
|
China
|
RiceFlow
|
2013
|
India
|
Government of India
|
2021
|
Indonesia
|
Khoiriyah et al. (2023)
|
2023
|
Japan
|
Government of Japan Ministry of
Agriculture, Forestry and Fisheries
|
2024
|
Pakistan
|
Government of Pakistan
|
2022
|
Paraguay
|
RiceFlow
|
2013
|
Philippines
|
RiceFlow
|
2013
|
South Korea
|
Statistics Korea
|
2024
|
Thailand
|
Fakkong and Suwanmaneepong (2016)
|
2016
|
United States
|
United States Department of
Agriculture
|
2023
|
Uruguay
|
Asociación Cultivadores de Arroz
|
2023
|
Vietnam
|
RiceFlow
|
2013
|
Developed Group
|
RiceFlow
|
2013
|
Developing Group
|
RiceFlow
|
2013
|
Source: Durand-Morat, Alvaro and Weiles, RiceFlow,
accessed various dates; USDA, ARMS, accessed August, 2024; Government of India,
“Cost of Cultivation/Production Estimates,” accessed April 6, 2024; Government
of Pakistan, “Rice Paddy Policy Analysis for 2022–23 Crop,” accessed October
29, 2024, Fakkong and Suwanmaneepong (2016), “Determinants of Profitability of
Rice Farming in Peri-Urban Area, Bangkok, Thailand,” accessed various dates;
Asociación Cultivadores de Arroz, “Costos productivos,” accessed various dates.
The model contains five elasticity values: the Armington
elasticity of substitution between country sources within the two rice
composites ( ), elasticity of substitution between the two
rice composites ( ), elasticity of total industry demand ( ), elasticity of supply for paddy rice ( ), and the price elasticity of supply for land
( ). The elasticity values are listed in the
table below.
Table E.6 Elasticity estimates
Elasticity
|
Value
|
Source
|
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5
|
GTAP
|
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3
|
Commission estimate, assuming the
elasticity of substitution between composites will be less than within
composites
|
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−1
|
Commission estimate
|
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0.5
|
Commission estimate
|
|
Varies
|
Calibrated by the model in the section below
|
Source: Aguiar, A., Chepeliev, M., Corong, E.,
& van der Mensbrugghe, D. (2023). “The Global Trade AnalysisProject (GTAP)
Data Base: Version 11,” accessed August 2023.
The model also uses tariff data from the United Nations
Conference on Trade and Development Trade Analysis Information System accessed
through the World Integrated Trade Solution database.
Average tariff levels between pairs of countries are computed as a simple
average of the ad valorem equivalent tariffs for all Harmonized Tariff
Schedule of the United States (HTS) 8-digit products relating to rice.
Most-favored-nation tariff rates are used by default, but in cases where two
countries have a trade agreement, the preferential rate is used instead.
The tariff rates used for the developed- and
developing-country groups are the trade-weighted average of the tariff rates
for the countries included in the groups. These tariff rates are in turn
calculated as a simple average of tariffs applied by a given developed or
developing country at the HTS 8-digit level. For the European Union and
Colombia, which have TRQ policies, ad valorem equivalent rates are calculated
by computing the total revenue from out-of-quota imports and dividing this
revenue by the total value of imported rice.
The TRQ information used for Japan in the model comes from
USDA Global Agricultural Information Network reports.
The model does not use current production subsidies because of a lack of
concrete data for countries included in the model. The information on India’s
export ban and government-to-government contracts comes from governmental
notices.
For the quantitative assessment of food security, the model
produces the change in daily caloric intake using the change in quantity of
rice consumption. The model uses this change to calculate the percentage change
in the USDA Economic Research Service calorie gap index for the individual
developing countries in the model and the developing-country aggregate group.
Model
Calibration
The model uses the baseline data and exogenous parameters
described in the section above to calibrate some of the supply and demand
parameters and elasticities. The factor input cost shares of production,
including the cost share of land rents ( ), are exogenous and remain constant.
Additionally, all elasticities are exogenous and remain constant in the model,
other than the elasticity of supply for paddy rice, which is calibrated
according to the following equation. is the elasticity of supply of land for paddy
rice production.
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|
|
In the following equations, the superscript “ ” denotes initial values. The supply initial
shift parameter ( ) is calibrated according to the equation
below.
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|
The model uses initial delivered prices, which are a
function of data inputs, including the producer prices, milling quantity
recovery rates and costs, and the costs imposed by government policies, to
calibrate the inner nest demand shift parameter ( ).
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|
|
Countries in the model do not always have domestic
production for aromatic rice. When this is the case, the demand shift parameter
above becomes undefined. In these cases, the model normalizes the demand shift
parameter around aromatic rice imports from India rather than the domestic
shipments of aromatic rice, which would be zero for countries with no aromatic
rice production.
The outer nest demand shift parameter is calibrated
according to the following equation. Subscripts “ ” and “ ” denote nonaromatic and aromatic rice types,
respectively.
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|
|
The aggregate expenditure on rice by each country is calibrated using the equation below. The
model assumes that expenditure on rice remains constant.
|
|
|
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