Did Meatpackers Collude to Raise Beef Prices?

The rhetoric has been loud — the data has been quiet. Today we put “packer collusion” on trial. The question isn’t whether spreads spiked (they did), or whether concentration exists (it does). The question is whether the spikes were the product of an agreement rather than the predictable result of herd cycles, external events and plant utilization.

The Charges

Count 1 — Price-fixing (Sherman Act §1). An agreement among packers to raise or stabilize prices for beef products, while simultaneously lowering prices for cattle.

Count 2 — Output restriction. An agreement to reduce slaughter/chain speeds to elevate wholesale prices.

Count 3 — Bid suppression/market allocation. An agreement to limit competition for cattle (fewer bids, coordinated schedules, regional allocation).

Legal standard (what must be proved):

• Agreement (express or tacit) + anticompetitive intent/effect
• Evidence must overcome alternative explanations (supply, utilization, demand, shocks).
• Civil = preponderance of the evidence; Criminal = beyond a reasonable doubt.

Prosecution’s Case (what would be needed to convict)

1. Direct evidence (smoking guns)

• Emails, messages, calls or meeting notes discussing prices/volumes/bid strategy/kill schedules.
• Trade-association sidebars where competitively sensitive forward data are exchanged.

2. Economic fingerprints that don’t wash out with fundamentals

• Sustained super-normal profits across multiple years after COVID/outages, materially above cost of capital.
• Spread persistence: live↔cutout spreads remain abnormally high once utilization normalizes.
• Price convergence: regional bid dispersion collapses nationwide even after controlling for freight, quality, weather, and distance to plant.
• Coordinated capacity discipline: parallel slowdowns or kill cuts not attributable to labor, maintenance, or regulation.
• Procurement anomalies: bid rotations, unusual “no-bid” patterns across buyers, punishment for undercutting.

3. Patterns around non-fundamental dates

• Spreads jump around communication or lawsuit milestones but NOT around observable shocks (fires, labor, black swan events).

Defense’s Case

(The Big Bad Beef Packer)

Fact: Wholesale & cattle track; as cattle prices rise and fall, so do wholesale ground beef prices.
Data points: Only major deviation occurred during COVID, when store shelves were cleared out and production capacity experienced a major shock.
Interpretation: Wholesale ≠ packer margin; category management, labor, and packaging explain much of the higher wholesale gap.

(The Big Bad Beef Packer)

Fact: Packer margins rise and fall with herd expansion, market shocks, and capacity constraints. Other oligopolies, such as wireless carriers, see similar price changes; albeit at a higher operating margin level.
Interpretation: If a beef “cartel” existed, you wouldn’t see loss years; losses are consistent with rising cattle costs outpacing cutout during tight supply + throughput frictions. Further, oligopolies see fierce competition, which, when kept in check, produce better quality and a cheaper value for consumers.

(Economic Research Service - Meat Price Spreads)

Fact: Wholesale to farm meat spreads have seen only modest increases over the past 50 years; COVID is the outlier. Retail to wholesale spreads tell a completely different story.
Interpretation: What farmers are paid for cattle and packers are paid for beef align across more than half a century (outside of COVID). Contrarily, the price consumers pay at the retail counter has been a runaway train.

(Economic Research Service - Meat Price Spreads)

Fact: From 1970 to 2025, packers’ % of the beef dollar has decreased 7%, farms’ % of the beef dollar has dropped 10%, while the retailer has increased 18%.
Interpretation: Prior to packer concentration concerns, 1970-1980, the packers’ % of the beef was 12%. From 1981 to 2025, the average drops to 9%. While a modest bump was seen in the years following the last cattle cycle downturn (2013-2015), the past 10 years were only 12%.

Observed facts that CONTRADICT a cartel:

• Spike & reversion: Spreads fall back as utilization recovers.
• Regional dispersion: Basis/bids vary by region and plant outages.
• Loss years: Public P/L shows red ink; cartels won’t tolerate that.
• Retail stickiness: Category pricing and costs break any 1:1 pass-through story.

Jury Instructions (how to weigh the case)

• Step 1: Do utilization, herd, and shocks explain the lion’s share of spread variation?
• Step 2: After controlling for these, is there a durable, unexplained elevation consistent with agreement?
• Step 3: Do we have direct communications or procurement anomalies consistent with coordination?
• Step 4: Do profits remain consistently super-normal across normal periods?

If the answer to Steps 2–4 is no, reasonable doubt remains.

Verdict

Count 1 (Price-fixing): Not Proven. The clearest spread spikes coincide with capacity shocks (Holcomb, COVID) and herd dynamics; spreads compress as plants normalize. No durable, post-shock elevation or persistent super-normal profits are shown in the long-run data.

Count 2 (Output restriction): Not Proven. Throughput reductions align with cattle availability/labor/safety/maintenance realities. We lack evidence of coordinated slowdowns absent operational and cattle inventory causality.

Count 3 (Bid suppression/market allocation): Not Proven. Regional dispersion and loss years undermine a national coordination story. Absent documentary evidence or procurement anomalies, the burden is unmet.

Final finding: The price behavior is best explained by herd cycles + utilization + retail stickiness. The prosecution has not cleared the economic or legal bar for collusion.

“Sentencing” (reforms that actually help)

Even with a “Not Proven” verdict, the system can work better. Apply remedies that lower volatility and raise trust:

1. Spread transparency: A consistent, public gross spread definition (Comprehensive Cutout + Drop − 5-Area Live) published monthly.
2. Data access: Encourage scanner data and regional basis publication, with clear methodology notes.
3. Voluntary labeling: Keep MCOOL program-based and auditable; don’t sell it as a price-lowering tool.
4. Targeted trade tools, not blunt ones: Avoid broad Section 232 quotas/tariffs that raise consumer prices on lean trim without fixing supply.

Closing

The market doesn’t need a scapegoat to explain beef prices. It needs throughput, better transparency, and a shared understanding of the facts.

When we measure the right things — herd size, utilization, spreads, and the composition of imports — the picture is coherent.

That’s the truth. It’s less thrilling than a soundbite and far more useful to ranchers, feeders, packers, retailers, policymakers, and consumers.

— Hyrum Egbert authors the biweekly “The Big Bad Beef Packer” newsletter, which takes a look at packinghouse truths, trends and tough questions.

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