Kumu / Econ & Trade
International Economics & Trade
Trade, tariffs, and exchange rates — learned by actually importing a product. Your team finds a product on Alibaba, prices it against Amazon, brands it, and sells it to your classmates. Every classmate gets $200 to spend. The contest ranks teams by quantity sold × profit per unit, before fixed costs.
Stage 0 — get GitHub working, before the project
The whole project lives in a shared repo: research, brand system, listing, model, both decks, and the prompt log all move by branch → pull request → review → merge. That stops dead if one teammate cannot commit and push, so every member gets GitHub working before the first milestone below — not on the night of it.
It comes in two parts: the account, then the workspace.
Part 1 — Get a GitHub account never used GitHub? start here
Create the account and learn the three verbs: add → commit → push. If you already have a GitHub account, use it and go straight to part 2. Any email works for your GitHub account; for the free GitHub Education benefits, add your school address under Settings › Emails and apply, which is optional and never graded.
Part 2 — Set up your workspace
Stand up your own public portfolio repo, built to the standard and named for you. The team repo below runs on exactly these mechanics — branches, commits, pull requests — and your personal repo is where your own work lands, this course and every one after it.
Part 1 in full: the Git mechanics walkthrough
Start Here → Git mechanics walks the whole workflow once: local versus remote, add → commit → push in the desktop app and on the command line, editing files straight on github.com, and fixes for the mistakes everyone makes the first time. Read it before part 2 — the workspace page assumes you can already save work to a repo, and does not re-teach the mechanics.
The team project — import something real
The rules are simple and the economics are not: find a low-cost wholesale product with a high retail price, survive the tariff, and convince your classmates to spend their $200 budget on your product instead of every other team's. Two 5-minute presentations show the result; a shared GitHub repo carries the work.
Product Scout, Brand & Sales Pitch Presentation #1
Shortlist 3 products from Amazon's top sellers, pick one, define your team's brand as a
design.json design system, and ship a fake Amazon listing — live on GitHub Pages —
that sells the class in 5 minutes.
Suppliers, Tariffs & Unit Economics Presentation #2
Top-5 Alibaba suppliers, real HTS tariff lookup (AI-suggested, officially verified), and the landed-cost model: wholesale + tariff + freight → profit per unit → what the class can actually afford. Excel workbook included.
Deliverables
Weights and due dates are in your course schedule.
| Deliverable | Format |
|---|---|
| Shortlist: top 3 candidate products | Email to your instructor |
| Final product + price | Email to your instructor |
| Presentation #1: Sales | 5 min to class + email |
| Presentation #2: Unit Economics | 5 min to class + email; instructor and peer review |
| Forms & surveys (3) | At presentation time |
| 360 intrateam peer review | Survey |
| Profit contest | Quantity sold × profit per unit (before fixed costs); the top teams earn extra credit |
Market rules: each student gets $200 to spend on other teams' products. Don't price above $200 (nobody can buy it); be careful pricing very low (you'd need huge volume). Your classmates are your entire economy, so every pricing choice is a bet on what they will pay.
How the team repo works
One GitHub repo per team — the same discipline as the Micro/Macro team case and the Performance Ratios portfolio repo. Slides, research, the brand system, the listing, the model, and the prompt log all live there and move by branch → pull request → teammate review → merge. No email attachments between teammates, ever.
| Folder | What lives there |
|---|---|
README.md | The brief: shortlist, final pick, and a profit hypothesis before you model anything |
research/ | Amazon top-5 and Alibaba top-5 prices — every number backed by a screenshot |
brand/ | design.json (your brand as data) + design-system.html (the one-page gallery) |
listing/ | The fake Amazon listing — a real page, live on GitHub Pages |
model/ | unit-economics.xlsx — landed cost, margin, scenarios |
presentations/ | Both decks, branded from design.json |
prompt-log.md · reflection.md | Every meaningful AI session; what AI got wrong and how you caught it |
Practice first
Econ Policy Lab
Impose a tariff on a live supply-and-demand model and watch who really pays it — the incidence question your Presentation #2 must answer for your own product.
AI Tools Lab
The field guide to coding LLMs: chat vs "code" tools, web vs desktop vs CLI, and why giving the AI your repo is the single biggest upgrade. Read it before your first AI build session.
Kumu knows this course
Ask the ✳ tutor to stress-test your product pick against the $200-budget market, explain landed cost, or play a skeptical classmate who doesn't want your dryer balls. Log the session — that's your prompt log.
Verify everything
Every number an AI hands you is a draft until you verify it: every price gets an Amazon/Alibaba screenshot, every tariff rate gets checked at hts.usitc.gov. An unverified number in a deck costs more than it saves.