Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

October 2012

ICTSD Programme on Agricultural Trade and Sustainable Development

Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

By Bruce Babcock, Iowa State University Nick Paulson, University of Illinois

Issue Paper No. 45

October 2012

l ICTSD Programme on Agricultural Trade and Sustainable Development

Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

By Bruce Babcock, Iowa State University Nick Paulson, University of Illinois

Issue Paper 45

ii

B. Babcock, N. Paulson – Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

Published by International Centre for Trade and Sustainable Development (ICTSD) International Environment House 2 7 Chemin de Balexert, 1219 Geneva, Switzerland Tel: +41 22 917 8492 Fax: +41 22 917 8093 E-mail: ictsd@ictsd.ch Internet: www.ictsd.org Publisher and Director: Programmes Director: Programme Team:

Ricardo Meléndez-Ortiz Christophe Bellmann Jonathan Hepburn, Ammad Bahalim

Acknowledgments This paper has been produced under the ICTSD Programme on Agricultural Trade and Sustainable Development. ICTSD wishes gratefully to acknowledge the support of its core and thematic donors, including: the UK Department for International Development (DFID), the Swedish International Development Cooperation Agency (SIDA); the Netherlands Directorate-General of Development Cooperation (DGIS); the Ministry of Foreign Affairs of Denmark, Danida; the Ministry for Foreign Affairs of Finland; and the Ministry of Foreign Affairs of Norway.

For more information about ICTSD’s Programme on Agricultural Trade and Sustainable Development, visit our website at http://ictsd.net/programmes/agriculture/ ICTSD welcomes feedback and comments on this document. These can be forwarded to Jonathan Hepburn at abahalim [at] ictsd.ch Citation: Babcock, Bruce; Nick Paulson (2012); Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries; ICTSD Programme on Agricultural Trade and Sustainable Development; Issue Paper No. 45; International Centre for Trade and Sustainable Development, Geneva, Switzerland, www.ictsd.org.

Copyright ICTSD, 2012. Readers are encouraged to quote and reproduce this material for educational, non-profit purposes, provided the source is acknowledged. This work is licensed under the Creative Commons Attribution-Noncommercial-No-Derivative Works 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/bync-nd/3.0/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. ISSN 1817 356X Front cover image: Use graciously permitted by Goodplanet.org from the film Home (2009). Copyrights belong to Yann Arthus-Bertrand. More information is available at www.homethemovie. org<http://www.homethemovie.org>.

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ICTSD Programme on Agricultural Trade and Sustainable Development

TABLE OF CONTENTS LIST OF ABBREVIATIONS AND ACRONYMS FOREWORD

iv v

EXECUTIVE SUMMARY 1 1.

INTRODUCTION

3

2.

THE CURRENT STATE OF US COMMODITY SUPPORT

5

3.

PROGRAM DESIGN 9

4.

MAGNITUDE OF EXPECTED PAYMENTS

11

5.

IMPACT ON PLANTING DECISIONS

18

6. CONCLUSIONS

22

ENDNOTES

23

REFERENCES

24

APPENDIX A. MATHEMATICAL PROGRAM DESCRIPTIONS

25

APPENDIX B: DATA AND SIMULATION MODEL

27

APPENDIX C. COMPETITION MATRIX AND WEIGHTING VECTORS

29

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B. Babcock, N. Paulson â&#x20AC;&#x201C; Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

LIST OF ABBREVIATIONS AND ACRONYMS ACRE

Average Crop Revenue Election

ADM

Actuarial Data Master

ARC

Agricultural Risk Coverage

CBO

Congressional Budget Office

FSA

Farm Services Agency

PLC

Price Loss Protection

RLC

Revenue Loss Protection

RMA

Risk Management Agency

SCO

Supplemental Coverage Option

STAX

Stacked Income Protection

ICTSD Programme on Agricultural Trade and Sustainable Development

FOREWORD Budgetary pressures in the United States may result in legislators changing key aspects of agricultural spending in the upcoming farm bill. In an environment of high farm incomes recipients of government funds are finding it increasingly difficult to justify the status quo. Trading partners of the US have long voiced their opposition to trade distorting elements of its agricultural policy.

The confluence of these factors may lay the groundwork for significant change.

Those close to the debate on U.S. agricultural policy in Washington D.C. have noted a near absence of discussion on WTO compliance. As one of the biggest agriculture spenders in absolute and per capita terms, the US has an impact on producers and consumers in other countries. The distortion caused to global trade by government policy may have been lower in recent years due, at least in part, to high international prices for key goods. However, many payments under proposed legislation are likely to remain and will perhaps be incorporated into a strengthened crop and revenue insurance programme. The crop prices used under such programmes will determine future budgetary outlays and may affect farmers’ decisions. The structure of payments under these programmes, especially for cotton, rice, dairy and sugar, could shift production and prices abroad. Moreover, if current prices face a downward revision, US subsidies could increase sharply, nearing their WTO ceilings or fiscal limits.

The WTO Doha Round trade negotiations included limits on domestic support for agriculture as a key element. Although the round is currently at an impasse, the domestic support elements of the negotiating document, or draft modalities, have stabilized. In this context, national policies enacted independently of discussions in Geneva are likely to have significant impact in both setting the tone of talks when they resume and farm output in the interim. A proposed move away from direct payments to more trade distorting ‘amber’ and ‘blue’ box spending would backpedal on important reforms enacted in the US since the 1990s.

American agricultural policy, particularly where it concerns trade, is arguably a compromise between the producers and law makers, even in the context of reform. Many law makers, their constituents and the Obama Administration have focused on the importance of improved nutritional outcomes from subsidies, environmentally sound agricultural management and reducing waste.

These are welcome steps in the right direction. However, as one of the most important traders of farm goods, US domestic policy plays an outsize role in global food security prospects, and the fate of large portion of vulnerable people in developing countries. A policy shift in the country often helps set the agenda elsewhere. An environment of fiscal accountability may be the right time for reform.

In the paper that follows, Bruce Babcock and Nick Paulson, leading experts on US commodity programmes, offer an analysis of how production of key farm goods will change due to proposals on the Farm Bill and the countries most likely to be affected by them. They offer insights on the development of US agricultural policy and the particularities of the current House and Senate proposals, including the proposed resolution of the Upland Cotton dispute between the US and Brazil through the Stacked Income Protection Plan. We hope that you find the paper as fruitful a contribution to the debate and the quest for solutions.

Ricardo Meléndez-Ortiz Chief Executive, ICTSD

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B. Babcock, N. Paulson – Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

EXECUTIVE SUMMARY Although there is still some uncertainty about exactly when a new US farm bill will be passed, the content of its commodity support provisions are well known. The direct payment program will be eliminated. Marketing loans will be maintained. And farmers will likely be allowed to choose between a new revenue insurance program and a price insurance program that is a modification of the current countercyclical payment program. In an attempt to come into compliance with the World Trade Organization’s cotton decision, US cotton subsidies will be provided through a new program call Stacked Income Protection (STAX).

The primary motivation for making these program changes is a desire by the US Congress to change the way that subsidies are delivered to farmers. Direct payments have fallen out of favor with both the public and farm groups because making payments to crop farmers when net farm income is at record levels is impossible to justify politically in the current economic climate. To increase the political acceptance of farm subsidies, Congress has focused on creating new programs that can be justified as providing farmers with a better ”safety net.” Supporters of farm subsidies believe that making payments to farmers when a “loss” occurs will increase the political viability of continuing farm payments. The Senate has passed its version of the farm bill, as has the House Ag Committee.

The names of the new programs being proposed illustrate the attempt by Congress to focus on safety net programs. The cotton program’s name shows that it will protect income. The Senate’s revenue insurance program is called Agricultural Risk Coverage. The revenue insurance program being proposed by the House Ag Committee is called Revenue Loss Coverage. Both bills propose a new crop insurance program called Supplemental Coverage Option. And the new countercyclical payment program offered by the House Ag Committee is called Price Loss Coverage.

One problem that Congress has had to overcome in developing these new safety net programs is that the current US crop insurance program already provides farmers with a high level of protection against income losses. Because crop insurance premiums are so heavily subsidized, between 85 and 90 percent of crop acreage is insured in the program. But crop insurance policies must follow sound insurance principles. To make sure that farmers have an incentive to take care of their crop, the policies have a significant deductible. And to guard against adverse selection, the prices used in the program reflect prevailing market prices at the time that farmers sign their insurance contracts. These sound insurance practices have provided Congress with two rationales for their new farm bill programs. The insurance deductible is now characterized as a “shallow” loss that needs to be covered. Hence the farm bill’s revenue insurance programs provide coverage on top of existing crop insurance coverage to reduce the insurance deductible. The fact that declines in market prices will be reflected in a reduction in the guarantees that crop insurance offers farmers opens shows that crop insurance cannot protect farmers against losses caused by declines in farm prices that last more than one year. Hence the need for a price insurance program that contains price guarantees that do not change for the life of the farm bill. In addition both revenue insurance programs set their guarantees using five-year Olympic averages of past prices so price declines are not immediately reflected in the guarantees.

The problem with designing programs that cover the risks that crop insurance does not is that they have the potential to influence farmer’s planting decisions. If the influence is great enough, then program-induced changes in US crop acreage will be reflected in trade flows and world prices, and have the potential to harm farmers in developing countries. The likelihood that the new programs will influence planting decisions is enhanced because payments in all the new programs are calculated using actual planted acreage. The overall thrust of the new farm bill is that decoupled direct payments that have minimal impact on planting decisions will be replaced by coupled safety net programs that potentially have a large impact on planting decisions.

ICTSD Programme on Agricultural Trade and Sustainable Development

Coupled farm program payments influence planting decisions both by increasing the overall profitability of farming and by changing the relative returns to planting alternative crops. Increased overall profitability from crop production will tend to increase total planted acreage which is then allocated between crops. Changes in relative returns will influence the share of total crop acreage planted to each crop. Thus, to estimate the potential impact of the proposed new farm bill programs on trade and prices requires an estimate of how they will affect expected returns to crop production. The average change in expected returns to planting, corn, soybeans, wheat, cotton and rice due to adoption of the proposed farm bill programs were estimated using a stochastic model that accounts for price and yield variability. The results indicate that if average prices stay at the levels projected by the US Congressional Budget Office (CBO), then the changes in expected returns from the program will be modest and lower than the direct payments that farmers currently receive. The overall increase in expected revenue minus variable production costs across the five modeled crops is 4.6 percent. This estimate assumes that corn, soybean, rice and wheat farmers choose the program option that maximizes their returns and cotton farmers have the STX program.

If average price levels rise above CBO projections, then the impact on expected returns of the new programs will decrease. If average prices decline by 15 percent per year for the first three years of the farm bill, then expected returns from the lower prices. Expected returns decrease by 51 percent with the programs in place. The current severe drought that is impacting major US production regions has increased prices substantially above CBO-projected levels. This suggests that it is more likely that average prices during the farm bill period will be higher than what CBO projects rather than lower.

The responsiveness of aggregate planted acreage in the United States to increased crop returns since 2006 has been low. A recent study used recent and estimated that a 50% increase in average returns to US crop production would lead to a 1.5% increase in total planted acreage. Use of this estimated responsiveness that the 4.6 percent increase in crop returns from the new farm bill programs using CBO average prices would increase aggregate planted acreage of the five modeled crops by 0.14 percent or by 233,000 acres. The smaller drop in expected returns due to the programs being in place under the low price scenario keeps 1.3 million acres in production that otherwise would have gone out of production.

The larger impact of the new farm bill program comes from their impact on relative returns between crops. Changes in the share of aggregate acreage for each of the model crops was estimated using a method developed by Matt Holt to allocate crops based on a competition matrix of own and cross price elasticities. Results show that the new programs will have the largest impact on cotton acreage because the cotton supply elasticity is higher than other for other crops and the programs increase expected returns to cotton by the largest percentage. This result implies that cotton producing countries, such as Brazil, India, Mali, and Pakistan would be affected most by the new US farm programs. In the low price scenario, acreage is allocated away from corn and soybeans to wheat as well because the target price for wheat is higher relative to market price than the other two crops. This results suggests that if prices drop, then wheat producing countries could be impacted as well by the new programs.

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B. Babcock, N. Paulson – Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

1. INTRODUCTION Although there is still some uncertainty about exactly when a new US farm bill will be passed, its provisions regarding support for US farmers are well known. The Senate-passed version of the farm bill eliminates the direct payment program, the ACRE program, and the countercyclical program. Taking their place in the commodity title of the farm bill is the Agricultural Risk Coverage (ARC) program that provides all program crops except cotton with new revenue insurance. In addition, the crop insurance title of the farm program contains three new programs. Cotton farmers will be able to buy additional insurance with the Stacked Income Protection (STAX) program.

Peanut farmers will have a new peanut revenue insurance program. And a new program, Supplement Coverage Option (SCO), provides farmers the option to buy additional insurance coverage above what they purchase through the existing crop insurance program.1

changes that will be made to better match program payments with a farmer’s own risk situation is to calculate program payments on the basis of planted acres. The Senate’s ARC program bases payments on base acres, and allows producers to choose county- or farm-level coverage for the program’s yield component.

The House RLC program bases payments on county yields and planted acres. The House PLC program allows farmers to update their program yields based on their average yield from 2008 to 2012 and bases payments on planted acres.

Although the House of Representatives has not passed its version of the farm bill, the House Agriculture Committee has passed a bill. It follows the Senate’s lead in eliminating direct payments and replacing it with a new revenue insurance program (called Revenue Loss Coverage or RLC) for crops other than cotton and it includes STAX for cotton. In addition, the House version proposes to keep the countercyclical program (now called Price Loss Coverage or PLC), raise the program’s trigger prices, update program yields and base payments on planted acres instead of base acres. The House version offer farmers the choice of PLC or RLC. If farmers choose PLC, then they can buy the supplemental insurance which is the same as SCO.2

Payments under both the current direct payment program and the countercyclical program are calculated using fixed base acres and program yields. Hence they are decoupled from farmers’ production decisions and hence have had minimal impact on farmers’ planting decisions. Recoupling program support increases the likelihood that US farmers will base their planting decisions at least in part on government prices and programs rather than just on market prices. The potential trade distortions caused by the PLC program relative to the current countercyclical program will be increased because payments will be based on planted acres and updated program yields rather than base acres and past program yields. This change combined with higher trigger prices raises the possibility of a significant incentive to expand production if a crop’s target price is above market prices. This recoupling of farm program support also has important WTO implications because all the new programs will be reported as Amber Box spending. Smith, Babcock, and Goodwin (2012) demonstrate how a drop in market prices with these new programs could result in the United States exceeding its Amber Box limits.3

The overarching thrust of all these new programs is to provide farmers with an improved farm safety net. With the exception of PLC, these new programs are variations of “shallow loss” programs that are designed to make payments supplemental to the “deep loss” payments from existing crop insurance products. Smith, Goodwin, and Babcock (2012) provide a good overview of shallow loss programs. One of the

Thus the proposed farm bill changes represent a step backwards in terms of efforts to only provide farmers with support that does not distort their planting decisions. If US farmers begin to base their planting decisions on government support rather than on the basis of market signals, then it is possible that farmers in developing countries will receive lower prices than they otherwise would because of

ICTSD Programme on Agricultural Trade and Sustainable Development

the supply-enhancing aspects of the new US farm bill. The prospect that US farm programs will distort the planting decisions of farmers is what motivates this study, which proceeds as follows. Section 2 describes the current state of commodity support in the US including the extent to which farmers participate in the crop insurance program. Section 3 describes how

the new programs work. Section 4 presents estimates of the magnitude of payments that the new programs will provide under different price projections. The extent to which the programs will alter US farmersâ&#x20AC;&#x2122; planting decisions and hence impact world market prices and developing countries is presented in Section 5. The paper concludes in Section 6.

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B. Babcock, N. Paulson – Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

2. THE CURRENT STATE OF US COMMODITY SUPPORT US farmers who produce program crops 4 are eligible for direct payments, countercyclical payments, and marketing loans. Direct payments are fixed payments that are made to eligible farmers without regard to which crops are planted,5 or market conditions.

Hence they are close to pure decoupled payments. Countercyclical payments are made when the season average price received by farmers falls below the program’s trigger price, which as shown in Table 1, equals a crop target’s price minus the direct payment rate. A quick comparison of current market prices given in the first two rows of Table 1 to the countercyclical trigger prices shows that with the exception of cotton, all market prices are far above the trigger price. This divergence of program prices from market prices is a good thing for opponents of farm price supports because the countercyclical program will not trigger payments. But many farm payment supporters argue that the trigger prices need to be increased to provide farmers support if market prices fall from

their current lofty levels. Marketing loans offer little or no incentive to change planting decisions because because crop loan rates are so far below market prices, as shown in Table 1. The 2008 farm bill also gave farmers the option to choose to participate in ACRE (Average Crop Revenue Election), which is a revenue insurance program that insures against declines in state revenue. The price used to set the ACRE guarantee is based on the previous two years of market prices. Hence high market prices are readily reflected in the insurance guarantee, which is why the ACRE price guarantee is so close to current market prices. Only a small proportion of farmers chose to participate in ACRE perhaps because the program was new. The significance of ACRE is that it was the first commodity program that incorporates elements of a revenue insurance program and the first commodity program that automatically increases support levels in response to increases in market price. Both of these attributes feature prominently in the new programs being proposed for 2012.

Table 1. The Current Market and Program Situation for Major US Crops a Corn

Cotton

Rice

Soybeans

Wheat

2011/12

6.25

0.885

14.30

12.450

7.24

2012/13

7.90

0.70

14.20

16.00

8.00

Target Price

2.63

0.7125

10.50

6.00

4.17

Direct Payment Rate

0.28

0.0667

2.35

0.44

0.52

Countercyclical trigger price

2.35

0.6458

8.15

5.56

3.65

Loan Rate

1.95

0.52

6.50

5.00

2.94

5.64

0.86

13.40

11.80

6.48

5.08

0.77

12.06

10.62

5.83

Current Market Conditions

b

Current Program Settings

ACRE Price for 2012

c

ACRE Trigger Price for 2012

d

Source: Authors a. Units are $ per bushel for corn, soybeans and wheat, $ per hundred pounds for rice, and $ per pound for cotton b. Current market conditions are the estimated average price received by farmers for each of the two marketing years as estimated in September, 2012 by the World Agricultural Supply and Demand Estimates, World Agricultural Outlook Board, USDA. c. Equal to the average of the 2010/11 and 2011/12 average price received by farmers as estimated by the National Agricultural Statistics Service of USDA. d. The ACRE trigger price is calculated as 90% of the ACRE price based on the 90% guarantee for the program. ACRE payments would be triggered at these price levels if actual yields were equal to the ACRE yield guarantee.

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ICTSD Programme on Agricultural Trade and Sustainable Development

In addition to support provided by commodity programs, program crops also receive support from the U.S. crop insurance program. About 90 percent of taxpayer subsidies flowing to the crop insurance program are accounted for by corn, soybeans, wheat, cotton and rice. Farmers who choose to insure their crop choose a coverage levels which determines the portion of the premium that taxpayers pay. Farmers who choose insurance based on their own yields (individual insurance) can insure up to 85 percent of the value of their crop. Coverage can increase to 90 percent for insurance based on county yields. None of the programâ&#x20AC;&#x2122;s administrative costs are reflected in the premium. All are paid separately by taxpayers. The program is administered

by USDAâ&#x20AC;&#x2122;s Risk Management Agency and is delivered to farmers by private crop insurance companies. Policies are sold and managed by private insurance agents. The government reimburses the companies for the cost of managing and selling the policies as well as loss adjustment costs. In addition, the companies receive highly subsidized reinsurance through the Standard Reinsurance Agreement.

Table 2 shows the proportion of premium paid by taxpayers for the different coverage levels and the proportion of planted acres in 2011 that were insured at each coverage level. As can be readily seen, a large proportion of U.S. crops are insured and, with the exception of rice, a large proportion of the crop is insured at fairly high coverage levels. About 70 percent of the 2011 corn crop was insured at coverage levels of 70 percent or above.

About 67 percent of the soybean crop and 56 percent of the wheat crop were insured at 70 percent or above coverage levels. At the 70 percent coverage level, a crop insurance indemnity will be paid to farmers if the farmlevel revenue or yield loss is greater than 30 percent. That is, the 70 percent coverage level is the same as a 30 percent deductible policy.

About 90 percent of acreage is insured against losses in revenue, which means that either a price decline or a yield decline or both can be the cause of the loss.6 The price used to set the insurance year is adjusted each year and reflects the level of harvest time futures prices at the time that insurance contracts are signed before the crop is planted.

Table 2. Level of Participation in 2011 in the U.S. Crop Insurance Program Coverage Level

Premium Subsidy

Corn

Cotton

Rice

Soybeans

Wheat

50%

67%

6%

22%

46%

8%

10%

55%

64%

0%

3%

2%

0%

1%

60%

64%

1%

18%

4%

1%

5%

65%

59%

7%

16%

7%

7%

16%

70%

59%

17%

19%

12%

17%

31%

75%

55%

23%

13%

10%

24%

17%

80%

48%

18%

2%

3%

17%

5%

85%

38%

9%

0%

0%

7%

3%

90%

44%

3%

0%

0%

2%

0%

Total for Crop

85%

93%

85%

85%

88%

Planted Area (million acres)

91.9

14.7

2.7

75.0

54.4

Proportion of Crop Insured at Given Coverage Level

Source: Authors Notes: All data taken from USDAâ&#x20AC;&#x2122;s Risk Management Agency Summary of Business reports. Premium subsidies are for optional unit coverage. If a farmer chooses to insure all land in a county as one insurance unit (an enterprise unit), then premium subsidies increase substantially. For corn grown in Iowa, enterprise unit premium subsidies range from a high of 80% at 70% and below coverage levels to a low 53% for 85% coverage.

The premium subsidy for 50% coverage is for insurance that pays out at 100% of price.

The premium subsidy for 90% coverage is for the area revenue (GRIP) and yield (GRP) insurance plans.

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B. Babcock, N. Paulson – Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

The US Congressional Budget Office (CBO) projects that the U.S. crop insurance program will cost taxpayers in excess of $8 billion per year over the period covered by the 2012 farm bill. Actual costs will vary from year to year depending on the severity of crop losses.

Given the high cost of the program, the large proportion of farmers who participate in the program, and the high coverage levels that farmers are buying, it is somewhat surprising that Congress has concluded that farmers need a stronger safety net to be provided by commodity programs. There are a number of reasons for why Congress has reached this conclusion. First, it seems that political support for the direct payment program has largely vanished because of the difficulty in justifying sending $5 billion per year to crop farmers who are experiencing record income levels because of high market prices.

Thus Congress needs a new way to deliver support to farmers that can be made to look more acceptable to the public. Second, the crop insurance program has a deductible that farmers absorb before the insurance is triggered. By definition, farmers who operate on thin margins may already be losing money on their crop before the insurance is triggered. Third, the distribution of realized losses in the crop insurance program has historically favored certain crops and regions.

For example, loss ratios for corn and soybeans in the Midwest have historically been lower than for crops grown in the South. Hence, some farm groups have called for an improved safety net that helps them manage the “shallow” losses that are not covered by crop insurance. It is fortuitous for farm groups that farmers’ expressed desire for help in covering their insurance deductible so neatly fits with Congress’ desire to find politically palatable way to deliver support to farmers.

Losses not covered by crop insurance because of the program’s deductible provide a readymade definition of a farm loss that can be used to justify payments from a new program.

Thus was born the new commodity programs designed to cover shallow losses.

One political difficulty in using direct payments to fund a new insurance program to cover shallow losses is that the benefits of such an insurance program will flow to all crops under the same set of rules. Examples of such rules include the proportion of the deductible that is targeted and the proportion of the loss that is covered. This means that all crops will benefit by roughly the same amount from the program, with variations in average payments based on the variability of yield and price for each crop. But direct payments disproportionately favor producers of crops grown primarily in the U.S. South, including rice, cotton and peanuts. Thus trading in the direct payment program that favors southern crops for a program that treats crops much more equally is not that appealing to southern farmers. This explains why members of Congress from the South have complained about the shallow loss program only “working” for corn, soybeans, and wheat.

The House farm bill placates these regional interests through the new price insurance program (PLC), which is simply a re-defined countercyclical program with higher target prices. Using the justification that such a program fills the need for insurance coverage in the event of losses caused by multipleyear declines in market prices that are not covered by crop insurance (recall that crop insurance prices are reset every year to avoid influencing planting decisions) the House increased target prices substantially for rice and peanuts to offset a greater proportion of their direct payment decline.

To summarize, Congress is poised to use funds from the politically-indefensible direct payment program to pay for new, more politically-palatable programs that can be defended as only making payments when a farm suffers a loss. These new insurance-like programs will likely be adopted despite the existence of a costly crop insurance program that provides effective insurance coverage to a large proportion of US crops. In fact, the “losses” that are defined by the new farm bill

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programs are purposely not covered by crop insurance because to cover them would distort farmers’ planting decisions. These include insurance deductibles that serve the purpose of making sure that farmers plant crops that are suitable for their farm and that planted crops are properly cared for, and “losses” caused by multiple year price declines which, of course, is like insuring a burning house

against losses from fire. Covering these sources of loss has the potential for distorting farmers’ planting decisions, which has the potential for distorting trade and world market prices and causing harm to producers in developing countries. Before turning to the extent to which the new programs may distort planting decisions, how the programs will actually work needs to be discussed.

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B. Babcock, N. Paulson â&#x20AC;&#x201C; Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

3. PROGRAM DESIGN It is fair to say that there never has been a more complicated set of programs being considered at any one time by Congress. The complexity comes about both in terms of the payment formulas and because farmers are going to have to make a one-time choice about program options with a poor understanding of how the programs actually work. Under the Senate farm bill farmers can choose between individual and county coverage under ARC, as well as additional subsidized insurance coverage through SCO Combining SCO with either ARC option does increase the deductible for SCO coverage from 10 percent to 21percent. Thus, only farmers choosing individual or area-based insurance at coverage levels below 80 percent would receive additional coverage from SCO when combined with either ARC option. Cotton farmers have it easier because they only have to choose the payment multiplier; a choice that should be easy given the 80 percent premium subsidy.

Under the House bill farmers will need to choose between revenue insurance coverage (RLC) and a target price program (PLC). If they choose PLC then they can also buy SCO with a 70 percent premium subsidy.

Farmers are going to have a difficult time choosing between these options because ARC

and RLC use prices to set guarantee levels that are based on a five-year average price. SCO uses the crop insurance price and PLC uses a fixed target price. If market prices are higher than target prices and higher than past market prices, then SCO is the preferred option because it bases coverage on the current high market price. If market prices drop significantly, then ARC and RLC are preferred to SCO if the Olympic average price is higher than market prices. PLC may be preferred by all farmers if market prices fall enough because the lower market prices will never be reflected in the price guarantees. Furthermore, the choice between commodity programs (i.e.

ARC, RLC or PLC) will be made on a onetime, irrevocable basis under both House and Senate bills, which highlights the importance of making an informed decision among these complex options. The next section provides more insight into these choices.

The new programs being proposed by the House and Senate and that are described in this section are as follows: Each of these is described in turn. A mathematical expression that describes how each programâ&#x20AC;&#x2122;s payments are calculated is provided in Appendix A.

Agricultural Revenue Coverage (ARC) (Senate) is a revenue insurance program that covers a portion of a farmerâ&#x20AC;&#x2122;s crop insurance deductible. It would be administered as an optional commodity title program under the Farm Services Agency (FSA). No premium will be charged for ARC coverage. Farmers electing ARC program coverage will be asked to choose between county-based coverage and farm based coverage. In addition, they will need to opt out of ARC completely if they want to buy expanded coverage under SCO. Payments under individual-ARC cover 65 percent of planted acres for per-acre whereas county-ARC covers 80 percent of planted acres. Revenue shortfalls between 79 percent and 89 percent of the program guarantee are covered.

Price Loss Coverage (PLC) (House) is a target price program that makes payments based on planted acres. It differs from the current countercyclical program because farmers can choose to update their program yields used to calculate payments to 90 percent of the average yield from 2008 to 2012. The payments are made on 85 percent of planted acres.

PLC target prices are shown in Table 3.

ICTSD Programme on Agricultural Trade and Sustainable Development

Supplemental Coverage Option (SCO) (Senate and House) will be administered as a crop insurance program under the Risk Management Agency (RMA). Farmers will be asked to pay 30 percent of the amount that is needed to cover expected program payments. That is, they will be receiving a 70 percent premium subsidy. For a farmer who selects ARC, SCO is designed to cover losses between the farmer’s crop insurance level and the 79 percent coverage level floor provided by ARC. For a farmer who does not select ARC, SCO is designed to cover losses above the farmer’s coverage level. Before an SCO payment can be made, however, the county must suffer a 10 percent loss.

Stacked Income Protection Plan (STAX) (Senate and House) is only for cotton and is designed to make the US cotton program comply with the WTO cotton ruling. The program will be administered by the Risk Management Agency and farmers will receive an 80 percent premium subsidy rate. The only difference between the Senate and House payment formulas is that the House version does not allow the price to be used in the insurance guarantee to fall below $0.6871 per pound of cotton. Cotton farmers would need to choose a payment multiplier between 0.8 and 1.2. This multiplier increases or reduces each farmer’s total amount of insurance. Given that the premium subsidy for STAX is so high most farmers will likely choose the maximum payment multiplier.

Revenue Loss Coverage (RLC) (House) is version of a shallow loss revenue insurance program. Under the House Ag Committee bill, farmers would choose between RLC and PLC. A farmer that chooses RLC could not participate in SCO. Other differences with ARC is that RLC only provides county based revenue insurance and in the calculation of the price used to set the revenue guarantee, RLC replaces a year’s market price with the PLC fixed target prices (given below) in the calculation of the Olympic average of the average price used to set the RLC revenue guarantee if the market price falls below the target price level in any year.

Table 3. PLC Trigger Prices for all program crops Wheat

$5.50/bu

Corn

$3.70/bu

Rice

$14.00/cwt

Soybeans Other Oilseeds

$8.40/bu $20.15/cwt

Peanuts

$535/ton

Dry Peas

$11.00/cwt

Grain Sorghum

$3.95/bu

Barley

$4.95/bu

Oats

$2.40/bu

Lentils

$19.97/cwt

Small Chickpeas

$19.04/cwt

Large Chickpeas

$21.54/cwt

Source: U.S. House of Representatives Proposal on Farm Bill

10

11

B. Babcock, N. Paulson – Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

4. MAGNITUDE OF EXPECTED PAYMENTS Both the Senate and the House Ag Committee’s farm bills eliminate decoupled direct payments, which are reported by the United States as unlimited Green Box payments, in favor of coupled programs that make payments based on planted acres and actual yields. All the new programs base payments on current planted acres and current market prices, although farms cannot receive payments on acreage exceeding their total base acreage.

The individual ARC program bases payments on farm-level yields. County ARC, RLC, SCO and STAX, base payments on county yields while PLC payments are made on base yields which may be updated. The use of countylevel yields and the limit on payment acres to total base acreage are the only aspects of any of the programs which maintain some degree of decoupling.

But the fact that the programs are coupled does not necessarily imply that the programs will have a significant effect on US farmers’ planting decisions, which is a necessary condition for the programs to affect world prices and other countries’ farmers. Planting decisions will be impacted if the programs create a large enough payment incentive for farmers to respond to the program rather than the market. A key measure of the extent to which programs incentivize a change in decisions is the increase in revenue that can be obtained from the program by increasing acreage to one crop or another. Thus before we can estimate the impact of the programs on planting decisions we need to estimate their impact on revenue.

There are two approaches that one can take to estimate the impact of a program on future payments. The first approach assumes a level of yield and price and then calculates the payments that would be made. The problem with this approach is that the level of yields and prices in the future cannot be known today. The alternative approach is to assume a distribution of future yields and prices and then estimate the distribution of payments

that are made conditional on the price and yield distribution. This second approach is the one we take here. Appendix A to this report documents the stochastic model that we used. Briefly, the model estimates program payments for each county in the United States with sufficient data. Both farm-level and county distributions of yields are estimated.

Program payments are estimated for the period 2013 through 2017. The average price used in the analysis is the price level assumed by the Congressional Budget Office (CBO) in their March 2012 projections. For illustrative purposes we also simulate some program payments assuming that prices move lower than assumed by CBO in order to learn how the programs respond differentially to lower prices.

Before presenting the results some general conclusions about the potential magnitude of the payments can be made just by an examination of the payment formula.

Individual ARC payments per planted acre cannot be greater than 6.5 percent of the product of the Olympic average of price and the Olympic average of yield because ARC only covers between 79 and 89 percent of this product and payments are made on 65 percent of acres. County ARC payments cannot be greater than 8.0 percent of a product of Olympic averages, and RLC payments cannot be greater than 8.5 percent. The actual expected payment from these programs will be much lower than these maximum payments unless the probability of receiving revenue insurance payments is very high. The only way that the probability of receiving a payment is very high is if the market price is far below the price used to set the revenue guarantee. Thus, we can conclude that the extent to which ARC or RLC will distort planting decisions is limited unless market prices unexpectedly fall.

With regards to SCO payments, because SCO works much like the current plans of areabased insurance, such as Group Risk Plan (GRP)

ICTSD Programme on Agricultural Trade and Sustainable Development

and Group Risk Income Protection (GRIP), albeit with higher subsidy levels, the degree to which SCO will distort planting decisions is only modestly higher than distortions caused by the current crop insurance program. Because SCO uses a crop insurance price to set guarantees, there cannot be a situation where the price used to set guarantees is much higher than what the market already provides farmers.

When combined with support from the marketing loan program, the only limits on PLC payments are those that limit overall commodity payments to farmers, which experience has shown are widely circumvented.

Hence, PLC likely has the most potential to distort planting decisions. However, if market prices are far above the PLC target prices,

then planting distortions caused by PLC will be insignificant. Distortions will likely occur when market prices at planting time are above but close to the target prices or market prices are below target prices. In this case, it is clear that PLC payments will be distorting.

Table 4 presents the baseline prices that are used in this analysis. The prices through 2010 are the actual season average marketing price for each year and crop as reported by NASS. The 2011 and 2012 prices are the USDAprojected season average price contained in the July World Agricultural Supply and Demand Estimates report. The 2013 â&#x20AC;&#x201C; 2017 prices are the CBO projected prices. ARC and RLC use the Olympic average of prices. These are presented in Table5 based on the full year prices.7

Table 4. Actual and Projected Crop Prices for Baseline Price Scenarios a Corn

Cotton

Rice

Soybeans

Wheat

2007

4.20

0.593

0.13

10.10

6.48

2008

4.06

0.478

0.17

9.97

6.78

2009

3.55

0.629

0.14

9.59

4.87

2010

5.18

0.815

0.13

11.30

5.70

2011

6.10

0.910

0.14

12.30

7.25

2012

6.50

0.720

0.15

14.00

8.00

2013

4.54

0.701

0.13

10.46

5.63

2014

4.65

0.707

0.13

10.69

5.69

2015

4.69

0.715

0.13

10.81

5.79

2016

4.71

0.719

0.13

10.84

5.87

2017

4.74

0.718

0.13

10.90

5.95

a Units are $/bu for corn, soybeans and wheat and $/lb for cotton and rice.

Source: U.S. Congressional Budget Office

Table 5. Prices Used to Set Guarantees for ARC and RLC a Corn

Cotton

Rice

Soybeans

Wheat

2013

4.48

0.68

0.14

10.46

6.32

2014

5.11

0.72

0.15

11.19

6.58

2015

5.27

0.75

0.14

11.35

6.19

2016

5.31

0.75

0.13

11.43

6.21

2017

5.15

0.71

0.14

11.27

6.24

a Units are $/bu for corn, soybeans and wheat and $/lb for cotton and rice.

Source: Calculated from Table 4.

12

13

B. Babcock, N. Paulson â&#x20AC;&#x201C; Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

The model used in this analysis calculates payments for 100 farms for each county for each of 25,000 price draws. The number of price draws was determined by up to 50 years of county yield data and drawing 500 prices for each year. Many different summary statistics of the results could be presented but because the purpose of this paper is to estimate the degree to which the 2012 farm bill could impact world prices and developing countries, the most relevant summary statistics are the change in expected revenue that farmers will receive from the programs. Thus, presented below for each program are the expected payments by crop for each of the five years of the farm bill (2013 to 2017) and an average expected payment across the five years.

The programs that do not reflect current year market conditions in the price used to set the guarantee include ARC, RLC, and PLC. For these three programs, payments will be much lower than what we report under the assumption that average annual prices exactly replicate CBO projections. Because expected payments for most programs under CBO prices are already quite modest, there is no purpose in reporting payments when prices are stronger than CBO projections. But if average prices weaken, payments from ARC, RLC, and PLC will increase significantly, and the potential for market distortions from the program is much greater.

Thus we simulate and report expected payment levels for these three programs in a declining price environment.

Table 6 reports average expected ARC payments for corn, soybeans, rice, and wheat, and STAX cotton payments. ARC payments are reported for both the farm level and county level

options. For corn and soybeans, the farm level payments were higher than the county payments in approximately 90 percent of the counties. For rice, farm level ARC payments were higher in 95 percent of the counties. For wheat, 75 percent of the counties had higher county ARC payments. Thus, if farmers believe that prices will follow CBO projections and if forced to choose between farm-level ARC and county-level ARC, most corn, soybeans and rice farmers would choose farm level coverage and many wheat farmers would choose county coverage. While the payment rate for farmlevel ARC coverage is lower (65% vs. 80%), the amount of farm-level yield variability is relatively much higher than county-level variability for crops like corn, soybeans, and rice. For wheat, there is still a relatively large amount of yield variability even at the county level. Therefore, the higher payment rate results in larger expected payments for wheat under the county-level option.

Expected payment levels for ARC are modest.

Average corn expected payments are about $15 per planted acre. This is less than corn direct payments, which average more than $20 per acre. This lower average cost is why the CBO judges that the Senate farm bill cuts spending on commodity programs. A coupled expected payment of $15 per acre at an expected corn yield of 150 bushels per acre is equivalent to a $0.10 per bushel price increase. With an expected price of $5 per bushel, this represents a 2 percent increase in expected price.

ARC payments for rice, soybeans and wheat generate similarly small equivalent expected price increases. While not zero, the magnitude of the incentive to plant more corn, soybeans, rice and wheat due to ARC payments is modest.

14

ICTSD Programme on Agricultural Trade and Sustainable Development

Table 6. Average Expected ARC and STAX Payments at CBO Baseline Prices Year 2013

2014

2015

2016

2017

Average

Farm Level

13.83

15.25

16.22

16.35

13.96

15.12

County Level

9.89

12.75

15.47

16.98

13.88

13.80

Farm Level

24.74

22.71

16.65

16.64

16.30

19.41

County Level

17.85

14.54

6.13

6.87

7.11

10.50

Farm Level

9.75

10.00

9.85

9.85

9.17

9.72

County Level

6.59

7.26

7.45

8.02

7.38

7.34

Farm Level

7.02

6.43

4.96

4.82

4.78

5.60

County Level

8.09

7.20

4.92

4.81

4.85

5.97

25.87

27.60

29.51

31.34

33.03

29.47

Corn

Rice

Soybeans

Wheat

Cotton STAX

All payments expressed in dollars per planted acre. STAX payments were calculated using the House version formula with a minimum price guarantee of $0.6861. CBOT baseline prices are above this minimum for each year, so STAX payments for the Senate version would be the same.

Source: Simulated by authors.

Average expected STAX payments for cotton are about double average corn expected payments. A $30 per acre expected payment with a national average yield of 700 pounds per acre represents a 4.2 cent per pound increase in expected price, which is a 6.1 percent price increase at a price of 70 cents per pound. This 6.1 percent increase is about three times as large as the equivalent price increase for corn, soybeans, rice and wheat. This demonstrates that the STAX program has the potential to increase cotton plantings at the expense of other crops, an issue that we examine in the next section.

Table 7 presents average expected RLC payments. RLC is a county level program. And it covers a lower band of the revenue distribution (75 to 85 percent versus 79 to 89 percent for ARC). But RLC covers a higher percentage of acres that ARC (85 percent versus 65 percent for individual ARC and 80 percent for county ARC). The net effect of these differences is that average RLC payments are quite a bit lower than ARC payments. The lower payments increase the distortionary effects of STAX payments for cotton because the payment to cotton relative to RLC payments to the other crops is higher than under ARC.

Table 7. Average Expected RLC Payments at CBO Baseline Prices a 2013

2014

2015

2016

2017

Average

Corn

$7.29

$9.54

$11.77

$13.03

$10.42

$10.41

Soybeans

$4.77

$5.26

$5.39

$5.83

$5.32

$5.31

Wheat

$7.31

$6.43

$4.33

$4.23

$4.26

$5.31

Rice

$11.50

$31.44

$11.73

$13.39

$13.95

$16.40

Source: Simulated by authors. a units all in $/acre

15

B. Babcock, N. Paulson â&#x20AC;&#x201C; Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

Average expected SCO payments net of farmer premium paid are presented in Table 8. These payments assume that farmers buy 75 percent Revenue Protection as their underlying crop insurance coverage and that they buy 90 percent SCO coverage. For corn, soybeans, and wheat, SCO payments (net of premium paid) are higher than ARC payments. This suggests that if the Senate farm bill were to become law, a significant number of farmers may opt out of the ARC, and buy SCO instead.

Also, they will be encouraged to do so by crop

insurance agents because SCO will be delivered as a crop insurance program. In addition, since SCO is an insurance program it is not subject to the proposed payment limitations for the ARC and RLC/PLC programs ($125,000 per entity in the House Bill, and $50,000 in the Senate Bill). The higher SCO payments do not automatically mean that they are more distortionary than payments from the farmlevel ARC program because SCO payments are based on county yields. Hence, they are not completely coupled.

Table 8. Average Expected SCO Payments at CBO Baseline Prices a 2013

2014

2015

2016

2017

Average

Corn

$16.96

$16.96

$16.96

$18.14

$20.36

$17.88

Soybeans

$9.72

$10.08

$10.00

$10.34

$11.38

$10.30

Wheat

$7.42

$7.51

$7.50

$7.69

$8.05

$7.64

Rice

$8.03

$8.28

$9.04

$10.12

$11.60

$9.42

Source: Simulated by authors. a units all in $/acre

Average PLC payments are presented in Table 9. In simulating PLC payments, it was assumed that all farmers for all crops would update their program yields. In reality, some farmers would find their old program yields to be higher and they would not update. Because the corn, wheat, and soybean PLC target price is so far below CBO projected prices, PLC payments for these crops are quite low. However, the high PLC target price for rice relative to CBO-projected rice prices makes average rice payments much

higher than under any other program option.

This dramatic increase in expected payment from PLC is consistent with the stated objective of House leadership to make the House farm bill more favorable to rice than to the other major crops.8 At an average yield of 6800 pounds per acre and a price of 14 cents per pound the average rice payments are equivalent to a price increase of 6.6 percent, which has the potential for creating a significant incentive for US farmers to plant more rice.

Table 9. Average Expected PLC Payments at CBO Baseline Average Prices a 2013

2014

2015

2016

2017

Average

Corn

5.60

4.35

3.96

3.82

3.58

4.26

Rice

73.55

61.29

62.10

60.88

58.30

63.22

Soybeans

1.48

1.10

0.93

0.89

0.81

1.04

Wheat

8.56

7.87

6.82

6.06

5.37

6.94

Source: Simulated by authors. a units all in $/acre

The magnitude of the average payments from the new Senate and House programs are rather modest overall.9 Only cotton STAX payments and rice PLC payments might be expected to create a significant incentive to plant more cotton and rice. And, as discussed above, if prices move higher during the period covered by the farm bill, which they seem likely to

do at least in the first year or two because of the severe US drought that is impacting 2012 production of corn and soybeans, then the actual payments that will be received by farmers from these programs will likely be lower than reported here. If this occurs, then SCO becomes a preferred option because the price guarantees will reflect the higher market

ICTSD Programme on Agricultural Trade and Sustainable Development

prices immediately whereas prices used to set RLC and ARC guarantees adjust to increase or declining prices over multiple years and the PLC program prices remain fixed even when market prices increase.

However, if prices move unexpectedly lower, then average payments from RLC, PLC, and

ARC could increase substantially. To simulate the effects of lower average prices on payment levels, the average prices shown in Table 10 were input into the model. These prices reflect a 15 percent decline in price levels from CBO projected levels in each of the first three years, and then remain at these lower levels for the final 2 years of the farm bill.

Table 10. Average Prices under the Lower Price Scenario a Corn

Cotton

Rice

Soybeans

Wheat

2013

3.86

0.596

0.110

8.89

4.79

2014

3.28

0.506

0.093

7.56

4.07

2015

2.79

0.431

0.079

6.42

3.46

2016

2.79

0.431

0.079

6.42

3.46

2017

2.79

0.431

0.079

6.42

3.46

Source: Simulated by authors. a units all in $/acre

ARC and STAX payments are presented in Table 11. The effects of using an Olympic average to determine the price used to set the ARC guarantees is reflected in the pattern of payments. Average expected payments increase rapidly at first when the price guarantee is greater than the average market price. Then as the lower market prices enter the Olympic average, the price guarantee decreases and so too does the average expected payment. For corn, payments reach a peak at $41 per acre in 2016. At 150 bushels per acre and a price of $2.79, this payment is equivalent to a price increase of almost

10 percent. Maximum soybean payments are equivalent to a 15 percent price increase; maximum wheat payments are equivalent to a 8 percent price increase; and maximum rice payments are equivalent to a 10 percent price increase. STAX payments for cotton do not follow the same pattern because of the floor on price used in that program. The $69 average STAX payment in 2017 is equivalent to a price increase of 23 percent at the lower prices shown in Table 10. Thus, if price move lower, ARC and STAX will create far stronger incentives for US farmers to plant crops than if prices follow CBO projections.

Table 11. Average Expected Farm Level ARC and STAX Payments at Lower Prices a Year Corn

2013

2014

2015

2016

2017

Average

21.82

34.76

40.51

40.76

29.84

33.54

Rice

37.49

50.78

53.23

44.88

35.86

44.45

Soybeans

14.90

22.19

26.21

25.05

18.73

21.41

Wheat

9.38

11.27

10.63

10.51

8.38

10.03

Cotton STAX â&#x20AC;&#x201C; House

31.01

48.34

65.11

67.17

69.17

56.16

Cotton STAX - Senate

23.51

25.07

26.79

28.44

29.94

26.75

Note: House STAX payments include a minimum price component for the revenue guarantee of $0.6861/lb. Senate STAX payment do not include a minimum price component. For the lower price scenario, the House minimum price is used to determine the STAX revenue guarantee for each year.

Source: Simulated by authors. a units all in $/acre

16

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B. Babcock, N. Paulson â&#x20AC;&#x201C; Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

Average expected RLC and PLC payments are presented in Tables 12 and 13. RLC payments follow the same pattern of ARC payments due to the effect of the Olympic average.

But now RLC payments are higher than ARC payments. The reason for this is that when the price used to set the revenue guarantee for ARC or RLC is below prevailing market prices, increasing yield variability decreases the likelihood of receiving a payment. To see why, consider what happens if there is no yield variability and prices are always below the price guarantee. Then there is a 100 percent chance that a payment will be received.

Adding in yield variability means that there is now a chance that a high yield will result in enough revenue to exceed the revenue guarantee. Adding in more yield variability increases the chances further, which is why

RLC payments are greater than ARC payments at the farm level.

PLC payments under the low price scenario are large because the fixed PLC target prices are higher than prevailing market prices.

Expected price increases that are equivalent to the maximum PLC payments in Table 13 are 24 percent for corn, 60 percent for rice, 46 percent for soybeans, and 35 percent for wheat. These results just demonstrate that adopting fixed target prices that are higher than prevailing market prices has the most potential to distort planting decisions and subsequent trade quantities and world market prices. The distortionary effects of the ARC and RLC programs are smaller for the later years in the low price scenario as the their guarantees adjust to the lower price levels over time.

Table 12. Average Expected RLC Payments at Lower Prices a 2013

2014

2015

2016

2017

Average

Corn

21.42

48.00

60.19

61.51

59.75

50.18

Rice

44.17

75.80

76.89

77.78

78.76

70.68

Soybeans

14.79

31.34

38.80

39.42

39.15

32.70

Wheat

11.86

15.51

16.20

16.39

16.76

15.35

Source: Simulated by authors. a units all in $/acre

Table 13. Expected PLC Payments: Lower Average Prices a 2013

2014

2015

2016

2017

Average

Corn

22.33

56.30

100.49

100.49

100.49

76.02

Rice

161.52

248.21

322.35

322.35

322.35

275.36

Soybeans

10.08

32.40

63.56

63.56

63.56

46.63

Wheat

23.27

41.83

59.31

59.31

59.31

48.61

Source: Simulated by authors. a units all in $/acre

ICTSD Programme on Agricultural Trade and Sustainable Development

5. IMPACT ON PLANTING DECISIONS All the programs considered here base their payments on planted acreage rather than on base acres. This re-coupling of program payments means that farmers’ incentives to grow a particular crop will be influenced by the level of expected payments that each crop may receive from the program. In addition, these programs also provide incentives to increase total planted acreage of all eligible program crops. While payments from the ARC and RLC/PLC commodity programs are limited to total base acreage and subject to total payment limitations, an increase in total acres will still lead to larger payments in years when payments are triggered for specific crops which do not exceed total base acreage for the farm.

Furthermore, no limitations on payments or eligible acreage exist for the SCO program.

There are two economic forces that will work to increase acreage because of coupled farm payments. The first is that payments increase the overall profitability of crop farming. This will tend to increase total planted acreage.

Barr, et al estimated the U.S. crop acreage response to the sharp increase in crop returns since 2006. The study estimated that a 50% increase in returns would lead to a 1.5% increase in total planted acreage. This estimate of overall planted acreage response is used in this study. That is, if farm programs increase expected crop returns by 20%, then total acreage planted to these five crops will increase by 0.6%.

The second economic force that drives crop acreage is competition between crops for land. Programs can affect this competition when they deliver different levels of payments for different crops. For example, expected payments to rice are much greater than other

crops. Thus adoption of PLC payments will create an incentive for US farmers to plant relatively more rice than other crops. The method by which we translate differential changes in crop returns (expected revenue minus variable production costs) into a change in the share of acreage allocated to each crop follows closely a method developed by Holt in 1999. The method requires specification of a “land competition matrix” that contains elasticities that are translated into coefficients of a linear equation that can be used to calculate how land share changes with a change in crop returns. This approach was implemented using national acreage data and national returns data. Details about the method are available upon request.

Ideally, modeling the competition for land between crops should be done at a more disaggregated level than at the national level, but such estimates are beyond the scope of this study.10 To implement the method requires an estimate of expected crop returns for each crop.

Expected market revenue for each crop in each county was set equal to the product of the average price used in the simulations and national trend yield for each crop. Variable production expenses were calculated by multiplying expected yield by per-unit variable production expenses that are reported below in Table 14. These per-unit production expenses were taken from January 2012 CBO baseline projections. The resulting expected returns for each crop are shown in Table 15 for both sets of average prices.11 To account for other crops grown in the U.S. a category of “other” crop also is included in the analysis with acreage equal to 78 million.

18

19

B. Babcock, N. Paulson â&#x20AC;&#x201C; Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

Table 14. Per-Unit Variable Production Costs 2013

2014

2015 $ per unit

2016

2017 1.981

a

Corn

2.005

1.984

1.974

1.973

Cotton

0.667

0.669

0.673

0.679

0.686

Rice

0.076

0.076

0.076

0.076

0.077

Soybeans

3.481

3.474

3.469

3.478

3.488

Wheat

2.794

2.789

2.790

2.810

2.835

a Units are bushels for corn, soybeans and wheat and pounds for cotton and rice.

Source: Calculated by authors from U.S. Congressional Budget Office baseline projections.

Table 15. Expected Crop Returns Expected Returns CBO Baseline

Lower Prices $/acre

Corn

414

118

Cotton

34

-165

Rice

380

25

Soybeans

308

125

Wheat

114

26

Aggregate

293

85

Source: Calculated by authors.

The method used to estimate the change in acreage from the new farm bill programs is to simply estimate the percentage increase in expected returns to each crop, which is then used to estimate the aggregate percentage returns to growing all of these crops. The aggregate change in expected returns is used to determine the overall increase in crop acreage. The crop specific percentage change in expected returns is used to estimate the change in acreage share for each crop. Planted acreage under the new farm bill then equals the new share multiplied by the new aggregate acreage.

The scenario that will likely change acreage the most at CBO baseline prices is if rice farmers choose the PLC program, all cotton farmers utilize the STAX program, and all other farmers choose the ARC farm-level

program. All payments are treated as if they are equivalent to expected price increases.

That is, the difference in degree of coupling between STAX, which pays out on county yields, and ARC, which pays out on county or farm yields, and PLC, which pays out on program yields, is not accounted for. Thus, the changes in expected returns that determine farmersâ&#x20AC;&#x2122; planting decisions overstate somewhat the actual incentive to increase acres.

The payments increase aggregate expected returns by 4.61 percent. Thus aggregate acreage of the five crops examined increases by 0.138% (.03 X .0461). Given CBO baseline acreage of these five crops 2015 under current programs of 232,567 thousand acres, the programs increase aggregate acreage of these five crops by 321 thousand acres. The allocation of this acreage to the various crops is presented in Table 16.

ICTSD Programme on Agricultural Trade and Sustainable Development

Table 16. Impact of Proposed Farm Bill Programs on Planted Acreage at CBO Baseline Prices Baseline Acreage

Acreage with 2012 Farm Bill Programs

Percent Change

Million acres Corn

89.77

89.77

0.01%

Cotton

10.85

11.32

4.36%

Rice

3.02

3.03

0.29%

Soybeans

76.43

76.21

-0.29%

Wheat

52.50

52.56

0.11%

Total

232.57

232.89

0.14%

Source: Estimated by authors.

Cotton has the largest percentage change in acreage. The reason for this is that the percentage change in revenue for cotton from the STAX program is the highest. In addition, the responsiveness of cotton supply is greater than for other crops. The acreage changes for the remaining crops are not economically significant.

These results show that the new farm program will have little impact crop acreage decisions for the major field crops of corn, soybeans, and wheat if average prices during the farm bill remain in the range projected by CBO. The STAX program will boost production modestly, which suggests that the only countries potentially affected by the new farm bill programs under this price scenario are cotton producing countries.

The effects of the US farm bill will be more significant if prices decline. To measure the effects of the programs with declining prices requires calculation of what acreage would be without the programs under the lower prices.

The change in expected returns calculated from Table 15 is used to calculate this new level of acreage. Aggregate expected returns under the low price scenario drop by about 71 percent. Given the 0.03 aggregate supply elasticity, this implies a drop in acreage

of 2.13 percent, or almost 5 million acres.

The first column of results in Table 17 shows crop acreage under this low price scenario without the 2012 farm bill programs in place.

The middle column of results show acreage levels if all farmers choose to participate in the PLC program, except for cotton farmers who have STAX. The results indicate that PLC boosts wheat acreage by about 6 percent and rice acreage by about one percent relative to their levels without the programs in place.

STAX boost cotton acreage by almost 13 percent. Corn and soybean acreage actually decline relative to their low-price baseline levels. This indicates that PLC benefits wheat and rice producers relatively more than corn and soybean producers. The results in Table 17 indicate that the 2012 farm bill programs will distort planting decisions of US farmers if prices drop significantly from CBO-assumed levels. U.S. cotton and wheat acreage would be significantly higher with the new farm bill programs than without. These higher acreage levels would result in lower world cotton and wheat prices. Developing countries that would be hurt by these lower prices are cotton and wheat exporters. Developing countries that export cotton include Brazil certain West African countries, and Central Asian countries.

Developing country wheat exporters include Argentina and India.

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B. Babcock, N. Paulson â&#x20AC;&#x201C; Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

Table 17. Impact of Proposed Farm Bill Programs on Planted Acreage under Low Price Scenario Baseline Acreage

Acreage with 2012 Farm Bill Programs

Percent Change

Million acres Corn

88.48

87.62

-0.97%

Cotton

7.52

8.49

12.93%

Rice

2.97

2.99

0.81%

Soybeans

78.75

76.96

-2.26%

Wheat

49.91

52.90

5.99%

Total

227.62

228.96

0.59%

The central finding of this report is that the recoupling of farm support to planted acreage in the new U.S. farm bill proposals will increase planted acreage of those crops which receive a higher guarantee relative to market price levels than other crops, particularly when market prices fall below support prices. The

list of crops to be covered by the new programs and some of the developing countries that produce these crops are shown in Table 18.

Id a systemic decrease in crop prices occurs sometime in the next five years then it is likely that at least some of these countries will be adversely impacted by the U.S. farm bill.

Table 18. Potentially Impacted Countries from U.S. Farm Bill by Commodity Countries Potentially Impacted Wheat

China, India, Pakistan, Argentina,

Corn

Brazil, Argentina, Mexico

Rice

China, India, Indonesia, Bangladesh, Vietnam

Soybeans

Brazil, Argentina, India

Other Oilseeds

Ukraine, Argentina, China

Peanuts

China, India, Nigeria

Dry Peas

India, Ethiopia, Ukraine

Grain Sorghum

India, Nigeria, Ethiopia

Barley

Ukraine, Argentina, Morocco, China

Oats

Argentina, Brazil, Ukraine

Lentils

India, Nepal, Bangladesh

Chickpeas

India, Myanmar, Pakistan

Note: List of impacted countries based on 2010 production levels as reported by FAOSTAT. Actual vulnerability to negative impact depends in part on the degree to which world prices are transmitted to domestic producers.

ICTSD Programme on Agricultural Trade and Sustainable Development

6. CONCLUSIONS If Congress manages to pass a new farm bill this year it seems quite likely that the direct payment program and the countercyclical program will be eliminated and replaced with new insurance-like programs. Farmers will likely be given a choice of additional revenue insurance coverage on top of their current crop insurance coverage or price insurance through a new target price program, or a combination of both. All new program options currently being considered would provide coupled support in that farmers can influence the size of their payment through their production decisions.

Replacing decoupled programs with coupled programs would seem to significantly increase the likelihood that farmers’ production decisions will reflect program provisions rather than market prices, thereby affecting aggregate production, world prices, and the incomes of developing country farmers. But experience over the last six years in which crop prices have been particularly strong indicates that the aggregate response of US crop acreage to profitability is quite inelastic. This suggests that overall production of US subsidized crops will not be significantly impacted by the new programs. Rather, the impact of the new programs will primarily be reflected in a change in the mix of crops that are planted.

If commodity prices stay high, then the only way that significant distortions to planting decisions could occur with the new programs is if target prices are increased too much. In the House bill, the rice target price and the floor on prices used to determine the House’s version of the new cotton program’s revenue guarantee levels are both close to current market prices. This closeness is reflected in significant average expected payments to both crops. At CBO price levels, the STAX program is estimated here to increase cotton planted acres by between four and five percent. Rice acres increase by smaller amount because US rice supply is quite inelastic. If commodity

prices rise over the life of the farm bill then payments from the new programs will play an insignificant role in determining farmers’ production decisions. Crop insurance programs will provide farmers with increasing levels of support in this price scenario. Farmer participation in the expanded crop insurance options will be expected if prices increase.

If prices decline significantly from CBOprojected levels, then payments from the new farm bill programs would increase significantly because the programs’ guarantee levels are either fixed or adjust slowly to changes in market prices. Under this scenario, the overall acreage response will be small, but farmers would be expected to move to crops that are treated relatively well by the new programs. Because of the proposed high target price for rice in the House bill and the floor put on cotton price guarantees under the STAX program, acreage will tend to expand in favor of these two crops.

In addition, the proposed wheat target price increases wheat revenue relatively more than corn and soybean revenues are increased by their proposed target prices. Hence under a low price scenario wheat acreage will increase at the expense of corn and soybean acreage.

The probability that commodity prices will move significantly lower than assumed by CBO has decreased significantly for the first year or two of the farm bill because of the severe drought affecting the US Midwest. The drought has caused current futures prices for corn, soybeans and wheat to soar. They will likely only moderate in the next year or two if US ethanol plants decide to suspend operations, which will occur only if the US government relaxes its mandates. This suggests that while it is possible that prices will decline to levels that would cause the proposed farm bill programs to negatively impact developing country farmers, it is not likely to do so until potentially the last three years of the five years to be covered by this farm bill.

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B. Babcock, N. Paulson – Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

ENDNOTES 1

Both the Senate and the House Ag Committee bill also mandate a new revenue insurance program for peanuts that provides peanut growers the same type of revenue insurance that is available for major field crops as part of the crop insurance program.

2

The House Ag Committee’s version of the farm bill has not been brought up for a vote of the full House because House leadership does not believe that enough Republicans will support the bill due to insufficient cuts to nutrition programs. A significant number of Democrats do not support of the Committee’s bill because cuts to nutrition programs are deemed too severe. It is likely that the programs discussed in this paper will serve as the starting point for negotiations when the farm bill debate resumes after the fall elections

3

Smith and Goodwin note that the current ACRE program can also cause Amber Box problems for the United States because of rising prices and higher trend yields.

4

Eligible crops include corn, wheat, soybeans, rice, cotton, peanuts, oats, barley, grain sorghum, sunflower, pulse crops and so-called minor oilseeds. This study analyzes the impacts of program changes on corn, soybeans, wheat, rice and cotton.

5

The one exception is that if farmers choose to plant fruits, vegetables, or tree nuts on program base acreage then direct payments will be lost. This restriction affects relatively few farmers in the major growing regions for program crops.

6

Almost all farmers buy a peculiar form of revenue insurance that is designed to cover losses from forward contracting. If the price at harvest is greater than the price at planting, this protection calculates loss payments at the higher harvest price. Thus farmers who suffer a yield loss can actually receive more income than they had originally projected at planting.

7

ARC and RLC as proposed would use as the average national price for each crop across the first five months of the marketing year as the actual price to determine payment levels.

Hence the 2012/13 five-month price will be known in the first part of January of 2013 so it can be used to determine payments for the 2012 program year.

8

Although not reported here, expected payments to peanut farmers from PLC are also much higher than for other crops, which is also consistent with the House wanting to make higher payments to crops grown in the South.

9

The distribution of payments is highly skewed because payments are truncated at zero. The 2015 average per-acre payments conditional on market price falling at least one standard deviation is $26.37, $180.10, $5.98 and $29.95 for corn, rice, soybeans and wheat respectively.

These average payments rise to $99.79, $253.20, $39.92, and $51.48 respectively if price falls two standard deviations. With prices being lognormally distributed, the probability that price falls one standard deviation is approximately 15 percent. The probability of a two standard deviation price drop is approximately one percent for all crops.

10 Two studies that have estimated aggregate costs include Smith, Goodwin, and Babcock, and Smith Babcock and Goodwin.

11 Expected returns were calculated for 2015.

12 In the context of this farm bill a five-year Olympic average is calculated by eliminating the highest and lowest values in the five years and then averaging the middle three years’ values.

ICTSD Programme on Agricultural Trade and Sustainable Development

REFERENCES Barr, K. B.A. Babcock, M.A. Carriquiry, A. M. Nassar, and L.Harfuch.“Agricultural Land Elasticities in the United States and Brazil”Appl. Econ.Perspect. Pol. 2011 33:449-462; doi:10.1093/ aepp/ppr011 Holt, M.T., “A Linear Approximate Acreage Allocation Model.” Journal of Agricultural and Resource Economics. 24(1999):383-397.

Smith, V.H., B.A. Babcock, and B.K. Goodwin. Field of Schemes Mark II: The Taxpayer and Economic Welfare Costs of Price Loss Coverage and Supplemental Insurance Coverage Options.”

Working Paper, American Enterprise Institute, September 12, 2012.

Smith, V.H., B.K. Goodwin, and B.A. Babcock. Field of Schemes: The Taxpayer and Economic Welfare of Shallow-Loss Farming Programs.” Working Paper, American Enterprise Institute, May 30, 2012.

Smith, V.H., and B.K. Goodwin. “The ACRE Program: A Disaster in Waiting.” Working Paper, American Enterprise Institute, November 3, 2011. Working Paper, American Enterprise Institute, May 30, 2012.

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B. Babcock, N. Paulson â&#x20AC;&#x201C; Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

APPENDIX A. MATHEMATICAL PROGRAM DESCRIPTIONS The notation is used to explain what triggers a payment under each program and the size of the subsequent payments begins with some definitions. : Actual farm yield : Olympic average of farm yield in last five years : Actual county average yield : Olympic12 average of county average yield in last five years : Expected county yield used by crop insurance programs : Crop insurance coverage level selected by farmer : Actual price using National Agricultural Statistics Service (NASS) data : Olympic average of NASS prices in last 5 years : Actual price using crop insurance definition of harvest price : Projected harvest time price using crop insurance definition : Target price that triggers payments under PLC program : Program yield under the PLC program : Planted acreageon a farm ARC Payments under ARC from individual coverage are given by the following formula:

Individual ARC coverage covers 65 percent of planted acres for per-acre revenue shortfalls between 79 percent and 89 percent coverage. County ARC covers 80 percent of planted acres.

Payments under county ARC are defined by:

PAYARC ,Co = 0.80AfarmMax[0.89PNASS ,olyYcounty ,oly - PNASSYcounty ,0]; PAYARC ,Co â&#x2030;¤ 0.08AfarmPNASS ,olyYcounty ,oly

ICTSD Programme on Agricultural Trade and Sustainable Development

SCO SCO will be administered as a crop insurance program under the Risk Management Agency (RMA).

Farmers will be asked to pay 30 percent of the amount that is needed to cover expected program payments. That is, they will be receiving a 70 percent premium subsidy. For a farmer who selects ARC, SCO is designed to cover losses between the farmer’s crop insurance level and the 79 percent coverage level floor provided by ARC. The payment formula for a farmer who selects ARC is

For a farmer who does not select ARC, SCO is designed to cover losses above the farmer’s coverage level. Before an SCO payment can be made, however, the county must suffer a 10 percent loss. The payment formula for a farmer who does not select ARC is

The part of the payment formula in brackets calculates the percent loss that the county suffers.

Thus the maximum payment equals the farmer’s deductible percentage multiplied by projected county revenue.

STAX The payment formula for the House STAX formula is given by

PAYSTAX = γ AfarmMax[0.9Max[0.6861,PCI ]Ycounty ,CI - PCIYcounty ,CI ,0] 0.8 ≤ γ ≤1.2; The parameter

PAYSTAX ≤ 0.2 γ AfarmMax[0.6861,PCI ]Ycounty ,CI

is a payment multiplier that the farmer chooses.

The payment formula for the Senate STAX formula is given by

PAYSTAX = γ AfarmMax[0.9PCIYcounty ,CI - PCIYcounty ,CI ] 0.8 ≤ γ ≤1.2;

PAYSTAX ≤ 0.2 γAfarmPCIYcounty ,CI

The only difference between the Senate and House payment formulas is that House version does not allow the price to be used in the insurance guarantee to fall below $0.6871 per pound of cotton.

RLC The payment formula for RLC is

PAYRLC = 0.85AfarmMax[0.85PNASS ,olyYcounty ,oly - PNASSYcounty ,0]; PAYRLC ≤ 0.085AfarmPNASS ,olyYcounty ,oly PLC The payment formula for PLC is

PAYPLC = 0.85AfarmYPLCMax[Ptrigger - PNASS ,0].

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B. Babcock, N. Paulson â&#x20AC;&#x201C; Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

APPENDIX B: DATA AND SIMULATION MODEL Historical county-level yield data from USDA NASS was collected for the five program crops, corn, cotton, rice, soybean, and wheat. Counties included in the analysis were those with reported planted acreage in 2010 and at least 25 years of yield history over the 1961 to 2010 time period. Counties with reported acreage and yields that were differentiated between irrigated and non-irrigated production practices were treated separately in the analysis, with revenue program payments estimated separately for each practice and a weighted average calculated for that county with the weights reflecting acreage allocated to each irrigation practice type. The important crops where data on irrigated yields are available are Nebraska corn and soybeans, Texas corn and cotton, and Kansas corn.

National-level yield histories for each crop were also collected over the same 50-year time period. All yield histories were detrended using a simple third degree polynomial regression function so that each historical yield observation was adjusted to an equivalent 2013 crop year level. To project expected trend yields for the 2013 to 2017 program years, a linear trend was fit to the county-level yield histories from 1980 to 2010, implying a fixed linear increase in expected yields each year.

For each crop and price policy scenario, a set of 25,000 price draws were simulated for each program year with expected levels set to those in the baseline and low price scenarios. For estimating STAX and SCO program payments the volatility, or standard deviation, of prices was calibrated to the implied volatilities used for the rating of 2012 crop revenue insurance policies. These volatilities were reduced by 15% in estimating the crop revenue program payments to reflect the lower volatility observed in the five-month NASS average prices for program crops relative to that implied by their futures and options markets.

The historical correlation between nationallevel yield deviations from trend and prices

was imposed for each crop and program year.

The 50-year series of national yield deviations from trend were stacked or concatenated 500 times to provide 25,000 potential yield outcomes. Ultimately, this stacking results in 500 simulated price realizations for each specific yield observed in the 50-year history.

Farm-level yields were simulated for each county and year using the county-level yield observations. The magnitude of farm-level yield variability was assumed to be accurately measured by RMA base yield protection rates for the 2012 crop year, reported in RMAâ&#x20AC;&#x2122;s Actuarial Data Master (ADM). To simulate farm-level yields for each county, a set of 100 normally distributed deviates were added to each county-level yield realization.

The normal deviates were parameterized to have a zero-mean, and a standard deviation calibrated such that the farm-level simulated yields (county-level yields plus the normal deviates) across all available years of county yield realizations from 1961 to 2010 results in an implied insurance rate for 65% yield coverage which exactly matches the RMA base rate in the county for the same level of coverage.

This approach can be interpreted as simulating farm-level yields in each year for 100 farms in each county.

Program payments were estimated for county ARC, RLC, and STAX at the county-level for each program crop. For STAX payments, each individual detrended county yield observation was matched with the corresponding yearsâ&#x20AC;&#x2122; 500 price draws. STAX payments were calculated for each of the 500 revenue realizations assuming the maximum coverage of 70 to 90% of the revenue guarantee (covering losses from 10% to 30%) and the maximum producer elected multiplier of 1.20. The revenue guarantee for each county and year was set equal to the product of the county trend yield and expected price, consistent with how guarantees are set for the GRIP insurance program. Reported program costs, or net payments, also assume an 80% subsidy level for the STAX program.

ICTSD Programme on Agricultural Trade and Sustainable Development

ARC, SCO, and RLC revenue program payments were calculated in a similar manner for the other program crops. Each individual detrended county-level yield observation was combined with 100 calibrated normally distributed yield deviates to generate 100 farm-level yield outcomes. Each of the 100 yield outcomes was then matched with the 500 simulated price realizations for the corresponding year. Revenue program payments were then calculated for each simulated revenue outcome, and averaged across the simulation values to generate an expected program payment for each county.

RLC and ARC program payments were defined to cover revenue losses as specified in their payment formulas shown in Section II. With the guarantee defined as the product of the 5-year Olympic averages of farm-level yield and national prices. Use of the 5-year Olympic average for the 2013 crop year requires price and yield histories beginning in 2007 for pric-

es (2007-2011 prices for the 2013 price component) and 2008 for yields (2008-2012 yields for the 2013 yield component). Actual national prices were used for marketing years 2007 to 2010. The 2011 marketing year average price for each crop was set equal to the midpoint of the most current USDA WASDE report range, and 2012 to 2017 prices were set equal to the expected levels assumed for the baseline and low price scenarios, respectively. Historic farm yields used to calculate individual Olympic averages were set equal to observed historical county yields when available, and 70% of trend yields when a historic county yield was unavailable for years from 2008 to 2010.

The 70% of county trend calculation follows the rules outlined for the proposed revenue insurance programs which allows for the use of 70% of the county yield when historic farmlevel yield records are unavailable. County trend yields were used in the Olympic average for the yield component of the revenue guarantee for future years (2012 to 2016).

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B. Babcock, N. Paulson â&#x20AC;&#x201C; Potential Impact of Proposed 2012 Farm Bill Commodity Programs on Developing Countries

APPENDIX C. COMPETITION MATRIX AND WEIGHTING VECTORS The competition matrix used in this analysis is given below Corn

Corn

Cotton

Rice

Soybeans

Wheat

Other

0.09000

0.00035

0.00000

-0.03940

-0.01300

0.00458

Cotton

0.04323

0.05000

0.00000

-0.01000

-0.01000

-0.08042

Rice

0.00000

0.00000

0.03000

0.00000

0.00000

-0.08494

Soybeans

-0.07006

-0.00014

0.00000

0.06000

-0.00100

-0.00142

Wheat

-0.12735

-0.00079

0.00000

-0.00551

0.20000

-0.11295

Other

0.21250

-0.03024

-0.00499

-0.03714

-0.53553

0.39317

Following Holt, the two weighting vectors that are needed to implement this procedure are as follows: Crop

Acre

Value

Corn

28.51%

49.21%

Cotton

3.60%

3.16%

Rice

0.95%

1.87%

Soybeans

24.46%

32.26%

Wheat

17.44%

8.21%

Other

25.04%

5.29%

SELECTED ICTSD ISSUE PAPERS Agriculture Trade and Sustainable Development US Farm Policy and Risk Assistance: The Competing Senate and House Agriculture Committee Bills of July 2012. By Carl Zulauf and David Orden. Issue Paper No. 44, 2012.

Net Food-Importing Developing Countries: Who They Are, and Policy Options for Global Price Volatility. By Alberto Valdés and William Foster. Issue Paper No. 43, 2012.

Trade Policy Responses to Food Price Volatility in Poor Net Food-Importing Countries. By Panos Konandreas. Issue Paper No. 42, 2012.

Trade Policy Options for Enhancing Food Aid Effectiveness. By Edward Clay. Issue Paper No. 41, 2012.

Possible Effects of Russia’s WTO Accession on Agricultural Trade and Production. By Sergey Kiselev and Roman Romashkin. Issue Paper No. 40, 2012.

Post-2013 EU Common Agricultural Policy, Trade and Development: A Review of Legislative Proposals. By Alan Matthews. Issue paper No. 39, 2011.

Improving the International Governance of Food Security and Trade. By Manzoor Ahmad. Issue Paper No. 38, 2011.

Food Reserves in Developing Countries: Trade Policy Options for Improved Food Security. By C. L. Gilbert, Issue Paper No. 37, 2011.

Global Food Stamps: An Idea Worth Considering? By Tim Josling, Issue Paper No. 36, 2011.

The Impact of US Biofuel Policies on Agricultural Price Levels and Volatility. By Bruce Babcock. Issue Paper No. 35, 2011.

Competitiveness and Development Una Evaluación De La Ayuda Para El Comercio En La Práctica. By Ricardo Paredes. Issue Paper No. 24, 2012.

Evaluating Aid for Trade on the Groung: Lessons from Nepal. By Ratnakar Adhikari, Paras Kharel and Chandan Sapkota, Issue Paper No. 23, 2011.

Evaluating Aid for Trade on the Ground: Lessons from Cambodia. By Siphana Sok, Cambodochine Dao, Chandarot Kang and Dannet Liv. Issue Paper No. 22, 2011.

Evaluating Aid for Trade on the Ground: Lessons from Malawi. By Jonathan Said, John McGrath, Catherine Grant and Geoffrey Chapman. Issue Paper No. 21, 2011.

Evaluating Aid for Trade Effectiveness on the Ground: A Methodological Framework. . By Ratnakar Adhikari. Issue Paper No. 20, 2011.

EU Climate Policies and Developing Country Trade Vulnerability: An Overview of Carbon Leakage-Sensitive Trade Flows. By ICTSD. Issue Paper No. 19, 2011.

The Allocation of Emission Allowances Free of Charge: Legal and Economic Considerations. By I. Jegou and L. Rubini, Issue Paper No. 18, 2011.

The Role of International Trade, Technology and The Role of International Trade, Technology and Structural Change in Shifting Labour Demands in South Africa. By H. Bhorat, C. van der Westhuizen and S.Goga. Issue Paper No. 17, 2010.

Trade Integration and Labour Market Trends in India: an Unresolved Unemployment Problem. By C.P. Chandrasekhar. Issue Paper No. 16, 2010.

The Impact of Trade Liberalization and the Global Economic Crisis on the Productive Sectors, Employment and Incomes in Mexico. By A. Puyana. Issue Paper No. 15, 2010.

Globalization in Chile: A Positive Sum of Winners and Losers. By V. E. Tokman. Issue Paper No. 14, 2010.

Practical Aspects of Border Carbon Adjustment Measures – Using a Trade Facilitation Perspective to Assess Trade Costs. By Sofia Persson. Issue Paper No.13, 2010.

Trade, Economic Vulnerability, Resilience and the Implications of Climate Change in Small Island and Littoral Developing Economies. By Robert Read.

Issue Paper No.12, 2010.

Dispute Settlement and Legal Aspects of International Trade Conflicting Rules and Clashing Courts. The Case of Multilateral Environmental Agreements, Free Trade Agreements and the WTO. By Pieter Jan Kuijper. Issue Paper No.10, 2010.

Burden of Proof in WTO Dispute Settlement: Contemplating Preponderance of the Evidence. By James Headen Pfitzer and Sheila Sabune. Issue Paper No. 9, 2009.

Suspension of Concessions in the Services Sector: Legal, Technical and Economic Problems. By Arthur E. Appleton. Issue Paper No. 7, 2009.

Fisheries, International Trade and Sustainable Development The Importance of Sanitary and Phytosanitary Measures to Fisheries Negotiations in Economic Partnership Agreements. By Martin Doherty. Issue Paper No. 7, 2008.

Fisheries, Aspects of ACP-EU Interim Economic Partnership Agreements: Trade and Sustainable Development Implications. By Liam Campling. Issue Paper No. 6, 2008.

Innovation, Technology and Intellectual Property Bridging the Gap on Intellectual Property and Genetic Resources in WIPO’s Intergovernmental Committee (IGC). By David Vivas-Eugui. Issue Paper No. 34, 2012.

The Influence of Preferential Trade Agreements on the Implementation of Intellectual Property Rights in Developing Countries. By Ermias Tekeste Biadgleng and Jean-Christophe Maur. Issue Paper No. 33, 2011.

Intellectual Property Rights and International Technology Transfer to Address Climate Change: Risks, Opportunities and Policy Options. By K. E. Maskus and R. L. Okediji. Issue Paper No. 32, 2010 Intellectual Property Training and Education: A Development Perspective. By Jeremy de Beer and Chidi Oguamanam. Issue Paper No. 31, 2010.

An International Legal Framework for the Sharing of Pathogens: Issues and Challenges. By Frederick M. Abbott. Issue Paper No. 30, 2010.

Sustainable Development In International Intellectual Property Law – New Approaches From EU Economic Partnership Agreements? By Henning Grosse Ruse – Khan. Issue Paper No. 29, 2010.

Trade in Services and Sustainable Development Facilitating Temporary Labour Mobility in African Least-Developed Countries: Addressing Mode 4 Supply-Side Constraints. By Sabrina Varma. Issue Paper No.10, 2009.

Advancing Services Export Interests of Least-Developed Countries: Towards GATS Commitments on the Temporary Movement of natural Persons for the Supply of Low-Skilled and Semi-Skilled Services. By Daniel Crosby, Issue Paper No. 9, 2009.

Environmental Goods and Services Programme Market Access Opportunities for ACP Countries in Environmental Goods. By David Laborde, Csilla Lakatos. Issue Paper No. 17, 2012 Facilitating Trade in Services Complementary to Climate-friendly Technologies. By Joy Aeree Kim. Issue Paper No. 16, 2011.

Deploying Climate-Related Technologies in the Transport Sector: Exploring Trade Links. By Rene Vosenaar. Issue Paper No. 15, 2010 Harmonising Energy Efficiency Requirements – Building Foundations for Co-operative Action. By Rod Janssen. Issue Paper No. 14, 2010 Climate-related single-use environmental goods. By Rene Vossenaar. Issue Paper No.13, 2010.

Technology Mapping of the Renewable Energy, Buildings, and transport Sectors: Policy Drivers and International Trade Aspects: An ICTSD Synthesis Paper. By Renee Vossenaar and Veena Jha. Issue Paper No.12, 2010.

Trade and Sustainable Energy International Transport, Climate Change and Trade: What are the Options for Regulating Emissions from Aviation and Shipping and what will be their Impact on Trade? By Joachim Monkelbaan. Background Paper, 2010.

Climate Change and Trade on the Road to Copenhagen. Policy Discussion Paper, 2009.

Trade, Climate Change and Global Competitiveness: Opportunities and Challenge for Sustainable Development in China and Beyond. By ICTSD. Selected Issue Briefs No. 3, 2008.

Intellectual Property and Access to Clean Energy Technologies in Developing Countries: An Analysis of Solar Photovoltaic, Biofuel and Wind Technologies. By John H. Barton. Issue Paper No. 2, 2007.

Regionalism and EPAs Questions Juridiques et Systémiques Dans les Accords de Partenariat économique : Quelle Voie Suivre à Présent ? By Cosmas Milton Obote Ochieng.

Issue Paper No. 8, 2010.

Rules of Origin in EU-ACP Economic Partnership Agreements. By Eckart Naumann. Issue Paper No. 7, 2010 SPS and TBT in the EPAs between the EU and the ACP Countries. By Denise Prévost. Issue Paper No. 6, 2010.

Los acuerdos comerciales y su relación con las normas laborales: Estado actual del arte. By Pablo Lazo Grandi. Issue Paper No. 5, 2010.

Revisiting Regional Trade Agreements and their Impact on Services and Trade. By Mario Marconini. Issue Paper No. 4, 2010.

Trade Agreements and their Relation to Labour Standards: The Current Situation. By Pablo Lazo Grandi. Issue Paper No. 3, 2009.

Global Economic Policy and Institutions Multilateral Negotiations at the Intersection of Trade and Climate Change: An overview of Developing Countriesʼ Priorities in UNCSD, UNFCCC and WTO Processes. By Manual A. J. Teehankee, Ingrid Jegou, Rafael Jacques Rodrigues. Issue Paper No. 2, 2012.

The Microcosm of Climate Change Negotiations: What Can the World Learn from the European Union? By Håkan Nordström, Issue Paper No. 1, 2009.

These and other ICTSD resources are available at http://www.ictsd.org

www.ictsd.org

ICTSD’s Programme on Agricultural Trade and Sustainable Development aims to promote food security, equity and environmental sustainability in agricultural trade. Publications include: • US Farm Policy and Risk Assistance. The Competing Senate and House Agriculture Committee Bills of July 2012. By Carl Zulauf and David Orden. Issue Paper No. 44, 2012.

• Net Food-Importing Developing Countries: Who They Are, and Policy Options for Global Price Volatility. By Alberto Valdés and William Foster. Issue Paper No. 43, 2012.

• Trade Policy Responses to Food Price Volatility in Poor Net Food-Importing Countries. By Panos Konandreas. Issue Paper No. 42, 2012.

• Trade Policy Options for Enhancing Food Aid Effectiveness. By Edward Clay. Issue Paper No. 41, 2012.

• Possible Effects of Russia’s WTO Accession on Agricultural Trade and Production. By Sergey Kiselev and Roman Romashkin. Issue Paper No. 40, 2012.

• Post-2013 EU Common Agricultural Policy, Trade and Development: A Review of Legislative Proposals. By Alan Matthews. Issue paper No. 39, 2011.

• Improving the International Governance of Food Security and Trade. By Manzoor Ahmad.

Issue Paper No. 38, 2011.

• Food Reserves in Developing Countries: Trade Policy Options for Improved Food Security.

By C. L. Gilbert, Issue Paper No. 37, 2011.

• Global Food Stamps: An Idea Worth Considering? By Tim Josling, Issue Paper No. 36, 2011.

• Risk Management in Agriculture and the Future of the EU’s Common Agricultural Policy. By Stefan Tangermann, Issue Paper No. 34, 2011.

• Policy Solutions To Agricultural Market Volatility: A Synthesis. By Stefan Tangermann, Issue Paper No. 33, 2011.

• Composite Index of Market Access for the Export of Rice from the United States. By Eric Wailes. Issue Paper No. 32, 2011.

• Composite Index of Market Access for the Export of Rice from Thailand. By T. Dechachete.

Issue Paper No. 31, 2011.

• Composite Index of Market Access for the Export of Poultry from Brazil. By H. L. Burnquist, C. C. da Costa, M. J. P. de Souza, L. M. Fassarella. Issue Paper No. 30, 2011.

• How Might the EU’s Common Agricultural Policy Affect Trade and Development After 2013? An Analysis of the European Commission’s November 2010 Communication. By Alan Matthews. Issue Paper No. 29, 2010.

• Food Security, Price Volatility and Trade: Some Reflections for Developing Countries. By Eugenio Díaz-Bonilla and Juan Francisco Ron. Issue Paper No. 28, 2010.

• Composite Index of Market Access for the Export of Rice from Uruguay. By Carlos Perez Del Castillo and Daniela Alfaro. Issue Paper No. 27, 2010.

• How Would A Trade Deal On Cotton Affect Exporting And Importing Countries? By Mario Jales. Issue Paper No. 26, 2010.

• Simulations on the Special Safeguard Mechanism: A Look at the December 2008 Draft Agriculture Modalities. By Raul Montemayor. Issue Paper No. 25, 2010.

• How Would a Trade Deal on Sugar Affect Exporting and Importing Countries? By Amani Elobeid. Issue Paper No. 24, 2009.

• Constructing a Composite Index of Market Acess. By Tim Josling. Issue Paper No. 23, 2009.

• Comparing safeguard measures in regional and bilateral agreements. By Paul Kruger, Willemien Denner and JB Cronje. Issue Paper No. 22, 2009.

• How would a WTO agreement on bananas affect exporting and importing countries? By Giovanni Anania. Issue Paper No. 21, 2009.

• Biofuels Subsidies and the Law of the World Trade Organisation. By Toni Harmer. Issue Paper No. 20, 2009.

About the International Centre for Trade and Sustainable Development, www.ictsd.org Founded in 1996, the International Centre for Trade and Sustainable Development (ICTSD) is an independent think-and-do-tank based in Geneva, Switzerland and with operations throughout the world, including out-posted staff in Brazil, Mexico, Costa Rica, Senegal, Canada, Russia, and China. By enabling stakeholders in trade policy through information, networking, dialogue, well-targeted research and capacity-building, ICTSD aims to influence the international trade system so that it advances the goal of sustainable development. ICTSD co-implements all of its programme through partners and a global network of hundreds of scholars, researchers, NGOs, policymakers and think-tanks around the world.