a hybrid model approach to estimating impacts of china’s ... · a hybrid model approach to...

A Hybrid Model Approach to Estimating Impacts of China’s Tariffs on U.S. Soybeans

Pat Westhoff ([email protected]), Univ. of Missouri

Byung Min Soon ([email protected]), Univ. of Missouri

Tracy Davids ([email protected]), Univ. of Pretoria

AAEA annual meetings, Atlanta, GA, July 22, 2019

mailto:[email protected]



The problem

• China’s 25% retaliatory tariff on U.S. soybeans disrupts normal trading patterns

• FAPRI’s nonspatial model of world markets is not well suited to examining a tariff on a single exporter

• Other common approaches also have limitations

U.S. soybean trade before and after China imposed tariffs on U.S. imports (mil. tons)

2016/17 Sep.-Aug. 2017/18 Sep.-Aug. 2017/18 Sep.-May 2018/19 Sep.-May

Exports to China 36.1 28.2 27.7 7.0

All other exports 22.8 29.9 20.4 28.8

Total exports 59.0 58.1 48.1 35.8

Average farm price $9.47 $9.33 $9.48 $8.47

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Sources: USDA FAS GATS; USDA NASS (September-May prices for 2017/18 and 2018/19 are simple averages of monthly prices).

Note: A 25% retaliatory tariff on U.S. soybean exports to China went into effect in July 2018

Limitations of common approaches

• Armington• Treats imports of goods from different countries as imperfect substitutes—

how true is that for a relatively homogeneous product like soybeans?

• “Small shares stay small” feature means it is hard for novel trading patterns to emerge

• Spatial equilibrium• Less commonly used today

• Corner solutions common; small changes in tariffs or transportation costs can result in very large changes in trade patterns

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Modified spatial equilibrium approach

• Developed by Davids (2017) to examine African maize markets

• Initiates trade based on spatial arbitrage, but with flexible arbitrage correcting parameter

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𝐸𝑖𝑗 =

0 𝑖𝑓 𝑃𝑗 < (𝑃𝑖 + 𝑇𝑖𝑗 ) × 1 + 𝑇𝑅𝑖𝑗

𝛽1(𝑃𝑗 − (𝑃𝑖 + 𝑇𝑖𝑗 ) × 1 + 𝑇𝑅𝑖𝑗 ) 𝑖𝑓 𝑃𝑗 > (𝑃𝑖 + 𝑇𝑖𝑗 ) × 1 + 𝑇𝑅𝑖𝑗

𝛽2 𝑃𝑗 − (𝑃𝑖 + 𝑇𝑖𝑗 × 1 + 𝑇𝑅𝑖j − 𝑘𝑖𝑗 ) 𝑖𝑓 𝑃𝑗 − 𝑘𝑖𝑗 > (𝑃𝑖 + 𝑇𝑖𝑗 ) × 1 + 𝑇𝑅𝑖𝑗

Eij represents exports from region i to region j

Pi represents the price in country i (exporting country)

Pj represents the price in country j (importing country)

Tij represents the cost of trade from country i to country j

TRij represents the import tariff in region j applied to products originating from region i

Hybrid approach used here

• Like Davids in many respects—includes arbitrage term

• Also includes price ratio term (somewhat like an Armington model)

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Mmx ,t = β0 + β1Pm ,t

PX ,t + TM t ∗ 1+TR t + β2max 0, Pm,t − PX,t + TMt ∗ 1 + TRt + 𝜀t , where

Mmx ,t represents imports into country m from country x,

Pm,t represents the price of the good in the importing country,

PX,t represents the price of the good in the exporting country,

TMt represents transportation and marketing costs, and

TRt represents the ad valorem tariff rate

Baseline (with tariff) soybean trade, 2019/20

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U.S. Brazil Argentina ROW Total by

exports exports exports exports importer

Baseline (with tariff) (Million metric tons)

China imports 16.2 67.2 7.2 3.2 93.8

U.S. imports n.a. 0.0 0.0 0.6 0.6

Brazil imports 1.2 n.a. 0.0 0.3 1.5

Argentina imports 0.4 0.1 n.a. 1.7 2.3

Rest of world imports 38.0 7.0 0.8 n.a. 45.9

Total by exporter 55.9 74.3 8.0 5.9 144.1

Note: This baseline was prepared in March 2019, based on information available at that time. Based on more current information, China’s total imports and imports from the U.S. might be lower (in part because of African Swine Fever effects on meal demand) and it now appears unlikely that Brazil will buy U.S. soybeans.

Scenario (no tariff) soybean trade, 2019/20

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Scenario (without tariff) (Million metric tons)

China imports 33.0 57.3 4.7 2.1 97.1

U.S. imports n.a. 0.0 0.0 0.6 0.6

Brazil imports 0.0 n.a. 0.0 0.3 0.3

Argentina imports 0.0 0.7 n.a. 1.7 2.4

Rest of world imports 26.7 12.7 3.6 n.a. 43.1

Total 59.7 70.7 8.3 4.8 143.5

Impact of tariff elimination, 2019/20

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Impact of tariff elimination (Change from baseline, million metric tons)

China imports 16.8 -9.9 -2.5 -1.1 3.3

U.S. imports n.a. 0.0 0.0 0.0 0.0

Brazil imports -1.2 n.a. 0.0 0.0 -1.2

Argentina imports -0.4 0.6 n.a. 0.0 0.1

Rest of world imports -11.3 5.7 2.8 n.a. -2.8

Total 3.8 -3.7 0.3 -1.1 -0.6


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China imports 16.8 -9.9 -2.5 -1.1 3.3

U.S. imports n.a. 0.0 0.0 0.0 0.0




Total 3.8 -3.7 0.3 -1.1 -0.6

For U.S., gain in exports to China exceeds reductions in exports to other destinations by 3.8 million tons


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China imports 16.8 -9.9 -2.5 -1.1 3.3

U.S. imports n.a. 0.0 0.0 0.0 0.0




Total 3.8 -3.7 0.3 -1.1 -0.6

For Brazil, exports to China decline more than exports to other destinations increase.


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China imports 16.8 -9.9 -2.5 -1.1 3.3

U.S. imports n.a. 0.0 0.0 0.0 0.0




Total 3.8 -3.7 0.3 -1.1 -0.6

Argentina has not been a major player in the China market, and it gains slightly more in sales to other destinations than it loses in sales to China.


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China imports 16.8 -9.9 -2.5 -1.1 3.3

U.S. imports n.a. 0.0 0.0 0.0 0.0




Total 3.8 -3.7 0.3 -1.1 -0.6

Note some changes in trade among the U.S., Brazil and Argentina. This trade has historically been very small, and these effects might not show up using an Armington approach. The U.S. sold 2 mil. tons of soybeans to Argentina between Sep. 2018 and May 2019, after not selling any to Argentina between 2013/14 and 2016/17. U.S.-Brazil trade, however, has not occurred.


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China imports 16.8 -9.9 -2.5 -1.1 3.3

U.S. imports n.a. 0.0 0.0 0.0 0.0




Total 3.8 -3.7 0.3 -1.1 -0.6

Finally, note that changes in total trade for each country are generally far smaller than the changes in bilateral trade .

U.S. and Brazilian soybean prices, 2018-2019

0

50

100

150

200

250

300

350

400

450

Jul. 17 Sep. Nov. Jan. 18 Mar. May Jul. Sep. Nov. Jan. 19 Mar. May

Do

llars

per

met

ric

ton

Paranagua, Brazil Louisiana Gulf

Average difference, July 2018-June 2019: $35/ton; peaked at $92/ton in Oct. 2018

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Average difference, July 2017-June 2018: $15/ton

Source: USDA Foreign Ag. Service, “Oilseeds: World Markets and Trade,” July 2019 for Paranagua price; Louisiana Gulf price from USDA’s Agricultural Marketing Service.

Changes in soybean prices caused by elimination of China’s tariff on U.S. soybeans

9.3

-4.9

-8

-6

-4

-2

0

2

4

6

8

10

12

2018/19 2019/20 2020/21 2021/22 2022/23

Perc

ent

U.S. farm China wholesale

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The baseline assumes a 25% tariff on U.S. soybeans sold to China. The scenario eliminates the tariff on March 1, 2019 (midway through the 2018/19 marketing year).

Changes in soybean prices caused by elimination of China’s tariff on U.S. soybeans

-8

-6

-4

-2

0

2

4

6

8

10

12

2018/19 2019/20 2020/21 2021/22 2022/23

Perc

ent

U.S. farm Argentina up-river Paranagua, Brazil China wholesale

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The baseline assumes a 25% tariff on U.S. soybeans sold to China. The scenario eliminates the tariff on March 1, 2019 (midway through the 2018/19 marketing year).

Changes in soybean sector prices in 2019 due to elimination of China’s tariff on U.S. soybeans

Soybeans Soybean meal Soybean oil

U.S. (Decatur) +8.7% +6.2% +0.8%

China (wholesale) -4.9% -3.9% -1.6%

Brazil (Paranagua) -3.1% +0.2% -1.3%

Argentina (FOB up river) +1.6% +2.3% -0.3%

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Notes:

China’s 25% baseline tariff applies to U.S. soybeans, but not to meal or oil.

These price changes imply changes in crushing margins—in the no tariff scenario, the crushing margin in the U.S. shrinks, resulting in less domestic crush and more export of raw soybeans. The reverse is true in Brazil.

Impact of eliminating China’s soybean tariff on U.S. marketing year average prices

2018/19 2019/20 2020/21-2022/23 avg.

Soybeans +5.0% +8.9% +7.9%

Corn +0.7% +2.2% +2.6%

Wheat +0.3% +1.2% +2.2%

Upland cotton +0.1% +0.5% +1.2%

Hay +0.1% +0.4% +0.9%

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Notes: These results are from a stochastic model of U.S. markets. U.S. exports in the model were adjusted to achieve the same absolute changes in soybean prices as in the bilateral trade model, but because the two models have slightly different baseline prices, the percentage change in soybean prices differs slightly from that reported on a previous slide.

Impact of eliminating China’s soybean tariff on U.S. livestock sector prices

2019 2020 2021-2023 avg.

Fed cattle (5-area direct steers) +0.1% +0.4% +0.6%

Hogs (51%-52% lean) +0.2% +0.6% +1.1%

Chickens (wholesale broilers) +0.5% +1.1% +1.4%

All milk +0.2% +0.7% +1.4%

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Impact of eliminating China’s soybean tariff on U.S. farm income

2021-2023

2019 2020 average

(Billion dollars, change from baseline)

Oilseed receipts 3.0 3.8 3.9

Other crop receipts 0.8 1.4 1.6

Total crop receipts 3.8 5.3 5.4

Livestock receipts 0.3 0.9 1.6

Government payments -0.3 -0.9 -0.9

Feed expenses 0.9 1.4 1.4

Rent to landlords 0.0 0.2 0.6

Other production expenses 0.0 0.2 0.6

Total production expenses 0.9 1.8 2.7

Other net farm income -0.1 0.4 0.6

Net farm income 2.9 3.9 4.0

Note: Other net farm income includes crop insurance indemnity payments, the

value of inventory changes, and other adjustments to farm income.

Final comments

• Approach provides a way to estimate impacts of a bilateral tariff on agricultural markets, with some strengths vs. other approaches

• A few caveats• Parameters of the bilateral trade model are assumed, not estimated

• The approach used to calibrate the sectoral model to the bilateral trade model results has limitations (e.g., shifts in export demand for commodities other than soybeans may not be fully captured)

• This analysis focuses on China’s retaliatory soybean tariff only—it does notconsider other tariffs resulting from the current trade disputes

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Thanks!

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• FAPRI-MU website: www.fapri.missouri.edu

• Follow us on Twitter: @FAPRI_MU

• To contact Pat Westhoff:• 1-573-882-4647 • [email protected]• @WesthoffPat on Twitter

This material is based upon work supported by the U.S. Department of Agriculture, Office of the Chief Economist, under Agreement #58-0111-18-024, and the USDA National Institute of Food and Agriculture, Hatch project number MO-HASS0024. Any opinion, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture nor the University of Missouri.

FAPRI-MU team:• Julian Binfield• Sera Chiuchiarelli• Scott Gerlt• Hoa Hoang

• Lauren Jackson• Byung Min Soon• Wyatt Thompson• Jarrett Whistance• Peter Zimmel

Soon to be joined by Seth Meyer, former chairman of USDA’s World Ag. Outlook Board!

http://www.fapri.missouri.edu/


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