Kumu / Micro/Macro Econ / Imperfect Competition
Imperfect Competition — Pricing Power Analysis
You are advising the seed company — a real firm, deliberately unnamed — which sells two products out of the same factory. One is commodity corn seed, where it is one supplier among many and takes the market price. The other is patented GMO seed, where it faces the entire market's demand curve alone. Same crop, same $130M of fixed costs, and a very different profit.
Your job is to model both markets, explain why the gap exists, put a dollar figure on what society pays for it, and take a position on whether that price is worth paying.
Optional this term
Optional this term and not graded; work it if you want the practice. Case 1, Perfect Competition, is the graded case; the stages, the repo and the method here are the same.
The economics, briefly
In the first case you advised a price taker: the market handed it a price and the only question was how much. Here the question changes shape. A firm facing the whole market's downward-sloping demand does not get to sell one more unit at the going price — it has to cut the price to move that unit, and the cut applies to every unit it sells. So the revenue from one more sale is less than the price of that sale. For linear demand the arithmetic is exact: marginal revenue falls at twice the slope of demand.
That single fact rewrites the decision rule. Produce while marginal revenue exceeds marginal cost, stop where they meet — and then read the price off demand, because demand is what tells you the most buyers will pay for the quantity you chose. Firms that read the price off marginal revenue instead price themselves at about a quarter of what the market would have borne, which is the most common error in this case and one an AI will make confidently if your specification is vague about it.
The two markets
| Non-GMO corn seed (price taker) | GMO corn seed (patent monopoly) | |
|---|---|---|
| Demand | Flat at P = $120/bag — one seller among many | P = 525 − 0.0000067·Q — the firm is the market |
| Costs | Both: TVC = a·Q + b·Q² → MC = a + 2b·Q, with a = $1; fixed costs $130M. GMO's b is 15× smaller — the biotech scales. | |
| Rule | P = MC | MR = MC, then price off demand |
| Scale (stipulated for this case) | ≈ 4M bags | ≈ 34.4M bags at ≈ $270 |
The case figures are stipulated for this case and calibrated to public data on the U.S. seed market; the sources are in the project README.
Why the company is not named
The firm is real, and its identity and every source are in the project README — one click, openly signposted. The scenario withholds the name because you are being asked to advise this company, not to report on it. Once you know who it is, the model you built stops being the source of truth: there is a real number one prompt away, produced from parameters that are not yours, and comparing your deadweight-loss figure against it is not a check — it is a substitution. Every case on this site follows the same rule. The market is named, quantified, and cited. The protagonist is not.
What you'll be able to do afterwards
- Derive marginal revenue from a demand curve and explain, mechanically, why it falls twice as fast
- Locate a profit-maximizing quantity under either market structure, and read the price off the right curve
- Measure market power with markup and the Lerner index, and say what those numbers mean to somebody who has never heard of them
- Quantify deadweight loss and explain why it is destroyed rather than transferred
- Argue a patent tradeoff with a number attached instead of an intuition
How the engagement runs
Two stages, same rhythm as the first case: specify the model before it exists, have AI build it from your spec, audit what comes back — then explain what it means and recommend something.
Brief, Spec, Build, Audit
State the question and a hypothesis you can be wrong about. Then specify both markets precisely enough to build from, have AI build the workbook, and audit it against your own validation rules.
Analysis, Memo, Prompt Log
Why marginal revenue sits below price, why the price comes off demand, what markup and Lerner measure, what the deadweight loss means — and a defended position on the patent, written to somebody who has to act on it.
The lab that makes the welfare geometry visible
Econ Policy Lab
The Econ Policy Lab draws consumer surplus, producer surplus, and deadweight loss as areas you can watch move. This case's hardest number, the surplus that simply vanishes each year, is one of those areas. Open the lab, impose a distortion, and watch the triangle appear; then go find the same triangle in your own model. It is also the independent cross-check for your Stage 1 audit, the way the Farm Profit Lab was in the first case.
Where AI fits, and where it doesn't
You draft first and AI reviews; the doer–reviewer dilemma explains why. The line for this case:
| Good uses — log them | Yours alone |
|---|---|
| Explaining MR, MC, markup, and deadweight loss until they click; quizzing you | The hypothesis, written before you model anything |
| Building the workbook from your committed spec; debugging what it returns | Writing the spec — and auditing the result against it |
| Attacking your draft argument on the patent tradeoff and finding its weakest claim | Writing the analysis, the memo, and the reflection |
Every AI-supplied number is a draft until you have checked it — and in this case there is a specific thing to check for. A model that has not been told marginal revenue is its own series will happily build you a price taker.