The Analyst's Path

Phase 12 · Finance Plus, AI and the quant-code track · free

Microeconomics & Market Structures for Analysts

EC1.01 · 18,771 words

You have already written the words "pricing power" and "moat" dozens of times in this program. Phase 4 taught you to back those words with a numbers test, a gross margin that refuses to crack through an input-cost spike, a market share that barely moves for a…

Learning objectives

By the end you can:

  1. Solve algebraic demand-and-supply systems for equilibrium price and quantity, apply comparative statics to a curve shift, and compute consumer and producer surplus.
  2. Compute price elasticity of demand by the point (calculus) method and the arc/midpoint method; use its sign and magnitude to predict the revenue effect of a price change; and compute cross-price and income elasticities, classifying goods by each.
  3. State and apply consumer theory (the budget constraint, indifference curves, and the tangency condition (MRS = the price ratio)) to solve a utility-maximizing consumption bundle, and read the direction of income and price effects on demand from it.
  4. Build a firm's short-run cost curves from a total-cost function (fixed, variable, average, and marginal cost), prove by calculus exactly where marginal cost crosses average variable and average total cost, and apply the shutdown rule versus the long-run exit rule.
  5. Solve a firm's profit-maximizing price and quantity under perfect competition, monopolistic competition, and monopoly (MR = MC universally, with P = MC as competition's special case) and derive the Lerner Index / inverse-elasticity pricing rule that links a firm's markup directly to the elasticity it faces.
  6. Solve perfect competition's long-run zero-economic-profit equilibrium (efficient scale, entry and exit, the number of firms) and monopolistic competition's long-run tangency equilibrium (the excess-capacity result); and compute a monopolist's deadweight loss against the competitive counterfactual.
  7. Solve the foundational objects of game theory, dominant strategy, Nash equilibrium, the Prisoner's Dilemma, Cournot quantity competition for n firms, Bertrand price competition, and a repeated-game collusion-sustainability condition (the minimum discount factor), from a stated payoff structure or a demand-and-cost system.
  8. Read market power the way regulators and careful analysts do: compute concentration measures (CR4, HHI) and place them correctly as an input to a structure judgment, never the verdict itself; apply the SSNIP / critical-loss test for market definition; and state the three degrees of price discrimination.
  9. Tie pricing-power economics explicitly to specific, already-established Phase-4 moat verdicts, reading each one as a statement about the elasticity of the residual demand curve and the market structure that produced it, without re-deriving the five-forces, moat-source, or Greenwald/CAP machinery those modules own.
  10. (Productivity objective: R10 duality.) Solve any of the above numerically in Python, equilibrium systems, elasticities estimated from a data table, Cournot/Bertrand/collusion conditions, payoff matrices, and use an AI assistant to scaffold such code or explain a concept, while keeping the by-hand solving skill the gate rewards and verifying every AI-drafted number against your own hand solve.

The duality, stated once (R10). Objectives 1–9 are the understanding gate: the mastery check rewards setting up the algebra and solving it correctly, not recognizing a term. Objective 10 is the productivity payoff you keep, every equilibrium in this module is exactly the kind of small, well-posed numerical problem an AI assistant will draft code for happily and well, and exactly the kind of clean, confident-sounding number it will occasionally get wrong in a way that is invisible until you check it against your own hand solve. A tool can draft the Python; it cannot buy you a pass on knowing, cold, whether the answer it returned is the one MR = MC actually implies.


Prerequisites & connections

Builds on. Ordinary algebra and the idea of a derivative as a slope (a marginal rate of change) are the only mathematical prerequisite; no prior finance-plus module is required, and nothing here assumes QM1.01's probability machinery. Where this module's engine explains something you already compute elsewhere, it says so directly: M2.01–M2.03's ROIC and DuPont decomposition measure a firm's realized returns; this module supplies the textbook cost-curve and market-structure engine that explains why those returns land where they do and for how long they can be expected to persist. M3.02's CAPM and M3.03's WACC hurdle reappear here as the "cost of capital" against which perfect competition's long-run zero economic profit is defined. Common Mistake 5 makes that equivalence explicit, because confusing it is one of the most consequential errors an analyst carries out of an introductory economics course.

This page is an excerpt

The full module runs to 18,771 words and carries the worked examples, the tables, the quiz that gates the next module and the spaced-repetition deck built from it. All of it is free and none of it needs an account.

Terms this module defines