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USDA’s Market Facilitation Program (MFP) provided $23 billion in 2018 and 2019 to U.S. farm producers affected by foreign retaliatory tariffs. A Government Accountability Office review found serious weaknesses in how USDA estimated trade damage and distributed the second round of aid: some 2019 estimates and crop payments exceeded the damage calculated under USDA’s own methods. Separate reporting on GAO-reviewed USDA records found shortcomings in compliance checks and transparency. Those findings support criticism of the program’s design and controls, but they do not mean every payment was invalid or that all improper payments were fraud.
What the Market Facilitation Program was meant to do
The U.S. Department of Agriculture created the MFP to assist producers affected by foreign retaliatory tariffs imposed after the United States raised tariffs in the 2018 trade conflict. The program paid producers for estimated economic harm associated with those tariffs. Across the 2018 and 2019 rounds, USDA provided $23 billion, according to the GAO’s 2021 review.
The MFP was an emergency response, not a calculation of each farm’s documented loss. Its payment formulas relied on estimates of trade damage and production or acreage data, so the choice of baseline and the way aid was allocated mattered. GAO’s central concern was that USDA’s 2019 approach was not sufficiently grounded in representative data or transparently documented, and that the resulting distribution did not consistently track estimated harm by crop.
How the payment formulas changed between 2018 and 2019
| Feature | 2018 round | 2019 round |
|---|---|---|
| Trade-damage baseline | For an eligible commodity, USDA used the retaliating country’s 2017 import value of that U.S. commodity, before the retaliatory tariffs. GAO considered this a defensible baseline. | USDA summed the highest import value for each product within a commodity over 2009–2018. The resulting figure could be greater than imports in any single year and was not transparently documented, GAO found. |
| Payment basis | USDA divided each commodity’s estimated trade damage by its 2017 production to set a per-unit rate. | USDA set a per-acre county rate using the weighted average damage of eligible crops and the county’s historical acreage in eligible crops. |
| Who could benefit from the allocation method | Payments followed estimated damage at the crop level, but the design excluded some nonspecialty producers affected indirectly. | County rates brought nonspecialty producers into the payment system, but the county’s crop mix influenced the rate for all such producers there. |
| Effect on same-crop producers | The per-unit crop rate linked payment rates to the commodity’s estimated damage. | Producers of the same crop could receive different rates in different counties, and a crop’s total payments could exceed or fall short of its estimated damage. |
The contrast is not simply that one year’s formula was sound and the next was not. The 2018 approach more directly tied crop payments to estimated damage but left out some indirectly affected nonspecialty producers. The 2019 county approach broadened coverage, while weakening the connection between a particular crop’s estimated damage and the payments received by its producers.
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Why GAO criticized the 2019 damage estimates
For 2019, USDA’s method selected the highest import value for each product within a commodity across a ten-year period, then added those selected values together. Because different products may have peaked in different years, the sum could exceed the retaliating country’s total imports of the commodity in any one year. GAO found this baseline exceeded the highest annual import value from 2009 through 2018 for 14 of the 29 eligible commodities.
Wheat illustrates the consequence. Under USDA’s selected baseline, estimated 2019 MFP trade damage for wheat was $836 million—more than three times the 2018 MFP wheat estimate and more than twice China’s 2017 import value of U.S. wheat. These are comparisons among USDA’s estimates and historical trade values, not proof that any individual producer received an improper payment.
GAO also found that USDA did not transparently document how it chose the 2019 baseline or its elasticity values, which are used to estimate how trade responds to price changes. The agency disagreed with some criticism of its 2019 methodology, as reflected in the agency responses and reporting. GAO nevertheless recommended stronger internal review so future economic analyses document their methods and use representative baselines.
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On the GAO report page, the recommendation remained open: as of February 2024, USDA had not revised or planned to revise its internal review process. GAO’s page also says inquiries in March 2025 and March 2026 had received no response. That is the latest status stated there; it does not establish whether USDA took action afterward.
How the 2019 county rates changed who received more
Because county crop mix affected the per-acre rate, two producers growing the same crop could receive different rates simply because they farmed in different counties. GAO also found that total payments for individual crops did not consistently match the estimated damage attributed to those crops.
- Corn: GAO estimated that 2019 MFP payments to corn producers were about $3 billion higher than estimated corn trade damage.
- Soybeans, sorghum and cotton: Payments to producers of these crops were below their respective estimated damages.
GAO’s estimated average 2019 corn payment rates also varied by region:
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| Region | Estimated average 2019 corn payment |
|---|---|
| South | $69 per acre |
| Midwest | $61 per acre |
| Northeast | $34 per acre |
| West | $29 per acre |
These are historical county-rate averages reported by GAO, not current payment rates. The distribution findings show why “overspent” needs qualification: the evidence is that USDA’s estimates and allocation could produce payments above estimated damage for some crops, not that every dollar was unwarranted or that a single verified measure of actual farm losses was exceeded.
What the oversight review found—and what “improper payments” means
InvestigateTV and Investigate Midwest reported in 2022 that USDA conducted compliance spot checks on 2018 payments, but GAO found weaknesses in how those checks were designed and tracked. The sample selection did not adequately reflect risk, the agency could not reliably track how many checks it completed, and the checks effectively stopped after the second MFP round as USDA shifted attention to the Coronavirus Food Assistance Program. GAO Director of Natural Resources and Environment Steve Morris told the outlets: “The way they set up their oversight didn’t allow for reliable results.”
The same investigation said USDA’s Farm Production and Conservation Business Center estimated nearly $800 million in improper MFP payments in financial reports reviewed by GAO. The figure is a USDA internal estimate reported by the journalists, not a new independent GAO calculation. The cited cases included inadequate application evidence, late or incomplete applications, and missing staff approval. An improper payment can result from administrative or eligibility problems; the estimate is not proof that the amount was stolen or that recipients committed fraud.
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Eligibility, distribution and public transparency
InvestigateTV and Investigate Midwest reported, citing GAO findings, that historically underserved farmers—including low-income, racial minority, veteran and beginning farmers—received 3.6% of MFP funds, while more than $163 million went to high-income farms. The reported $900,000 income limit had an exception: an individual with at least 75% of income from farming could qualify. The investigation said USDA relied on documentation from attorneys or accountants rather than independently verifying the source of income.
The public payment data also offered less detail than USDA’s internal records. The public spreadsheets did not identify the crop tied to an individual payment or provide the farm’s physical address. To map payments, the journalists analyzed 24 USDA spreadsheets covering 2018–2020, grouped more than 3 million rows into about 754,000 unique recipient name-and-address pairs, and filtered the data to MFP non-specialty-crop payments. That subset represented 94% of MFP funds, but the map is not a complete disclosure of farm locations or crop-by-crop payments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How the program was funded and shaped
InvestigateTV and Investigate Midwest reported that the administration used the Commodity Credit Corporation (CCC), a USDA government corporation, to fund MFP under the Agriculture Secretary’s discretionary authority. The outlets described that route as allowing the department to design and administer the aid quickly without program-specific congressional approval.
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The investigation also reviewed USDA emails obtained through public-records requests and letters from commodity groups and lobbyists seeking inclusion or changes in the second round. It reported that crops advocated for by those groups ultimately appeared in that round. This is the journalists’ finding from their review of correspondence, not a conclusion in GAO’s economic analysis.
What the findings establish
GAO’s report supports a specific accountability conclusion: USDA used a 2019 trade-damage baseline that could exceed any one year’s imports, did not transparently document key methodological choices, and distributed county-based payments in ways that could diverge from estimated crop damage and vary for producers of the same crop. The 2022 investigation adds reporting on weakly tracked compliance checks, reported eligibility and administrative problems, limited public payment detail, and the distribution of funds.
Together, these findings show why the MFP’s speed and scale came with consequential risks to accuracy, consistency and oversight. They do not establish that all aid was unnecessary, that every payment was improper, or that the USDA internal improper-payment estimate represents fraud.
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