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Economist — Competency Roadmap

Work towards being an economist: developing the microeconomic, macroeconomic, mathematical and econometric foundations needed to analyse economic questions and produce evidence about them.

This curriculum provides an end-to-end foundation in modern economics, taking you from introductory calculus and core micro/macro models to advanced causal inference, dynamic macroeconomic modelling, and sector-specific applications across academia, central banking, and economic consulting. Because economics relies on analytical rigor, each phase establishes theoretical frameworks and mathematical derivations before testing them against empirical data using Python, R, and Stata workflows. At 12 hours per week the tasks here come to roughly four to five months of structured study. That is the map, not the route: economics is normally reached through formal study over several years, and this shows what that study covers. Finishing this curriculum leaves you able to formally derive standard economic models, implement quasi-experimental econometric designs on real-world microdata, and write publication-grade empirical research and policy evaluation reports.

By the end: You will be able to formulate and solve mathematical economic models across micro- and macroeconomics, design and execute causal econometrics strategies (such as Difference-in-Differences and Instrumental Variables) in R or Python, and produce a complete empirical working paper or policy evaluation brief with reproducible code.

Starting levelBeginnerStyleTheory first
12h / week11 phases42 tasks~225h total

This is the map — make this roadmap yours

It shows what this journey generally looks like. Tell Kaidoro your version of the goal and it builds the plan around where you are actually starting, what to do first, the hours you really have, and what you have already finished.

1

Mathematical Foundations for Economic Analysis

Economics requires multivariable calculus, linear algebra, and constrained optimization to formalise human decisions and market equilibria. This phase builds the mathematical toolkit needed to understand theoretical proofs and econometric derivations.

  • Master single and multivariable differential calculus
    ~5hLearn2 resources

    Microeconomic and macroeconomic models describe marginal decisions through derivatives.

    You'll learn

    • Partial Derivative — the rate of change of a multivariable function with respect to one variable while holding others constant
    • Hessian Matrix — the square matrix of second-order partial derivatives describing local curvature
    • Implicit Function Theorem — a mathematical rule allowing derivatives of implicitly defined relations to be computed directly

    Review single-variable derivatives and expand into multivariable calculus. Focus on partial derivatives, the chain rule in multiple dimensions, gradients, and total differentials, which economists use to model marginal changes in multi-agent environments.

    Done when: you can calculate partial derivatives, gradients, and Hessian matrices for multivariable polynomial and Cobb-Douglas functions without computational software.

    How to work through it

    1. Review single-variable power, product, quotient, and chain rules
    2. Calculate first-order and second-order partial derivatives for multivariable functions
    3. Construct Hessian matrices and evaluate their definite status for concavity checks
    4. Solve total differentials for implicit functions using the Implicit Function Theorem
  • Solve unconstrained and constrained optimization problems
    ~6hPractice

    Constrained optimization is the core analytical engine behind consumer choice, firm cost minimization, and optimal policy design.

    You'll learn

    • Lagrangian Function — an auxiliary function combining an objective and constraints scaled by multipliers
    • Lagrange Multiplier — the shadow price representing the marginal value of relaxing a constraint
    • KKT Conditions — first-order necessary conditions for non-linear optimization with inequality constraints

    Economists model economic actors as maximizing utility or profit subject to constraints. Learn unconstrained first-order conditions (FOC) and second-order conditions (SOC), then move to constrained optimization via Lagrange multipliers and Karush-Kuhn-Tucker (KKT) conditions for inequality constraints.

    Done when: you can solve an analytical utility-maximisation problem subject to a linear budget constraint using Lagrange multipliers and verify the second-order sufficient conditions.

    How to work through it

    1. Derive first-order necessary conditions for unconstrained multi-variable functions
    2. Set up the Lagrangian function for equality-constrained problems
    3. Derive first-order conditions and solve for optimal analytical values and the shadow price
    4. Formulate Karush-Kuhn-Tucker (KKT) conditions for inequality constraints
  • Work with matrix algebra and linear systems
    ~5hLearn1 resource

    Econometric estimators and multi-market models cannot be written compactly or implemented computationally without matrix algebra.

    You'll learn

    • Matrix Rank — the maximum number of linearly independent column or row vectors in a matrix
    • Positive Definite Matrix — an analogue to a positive real number ensuring strict convexity in optimization
    • Cramer's Rule — an explicit formula for the solution of a system of linear equations

    Learn matrix operations, matrix inversion, determinants, eigenvalues, and positive-definiteness. Matrix notation is the standard language of both intermediate econometrics (OLS in matrix form) and general equilibrium models.

    Done when: you can solve a 3x3 system of linear supply-and-demand equations using matrix inversion and Cramer's rule by hand.

    How to work through it

    1. Perform matrix addition, multiplication, transpositions, and scalar operations
    2. Compute determinants and evaluate matrix rank to test for invertibility
    3. Solve linear equation systems Ax = b using matrix inverses and Cramer's Rule
    4. Examine eigenvalues and trace to verify stability in dynamic systems
  • Build a mathematical optimization problem set in Python or R
    ~4hBuild1 resource

    Bridges pure pen-and-paper mathematics with computational economic modelling.

    You'll learn

    • SymPy — a Python library for symbolic mathematics used to derive analytical solutions
    • SciPy Optimize — a module providing numerical routines for constrained and unconstrained minimization
    • Constant Elasticity of Substitution (CES) — a standard generalized functional form used in production and consumer theory

    Solidify mathematical theory by writing a documented notebook that solves three canonical economic optimization problems symbolically (using SymPy in Python or Ryacas in R) and numerically (using SciPy.optimize or R's optim).

    Done when: the script symbolically derives demand functions from a Constant Elasticity of Substitution (CES) utility function and computes numerical solutions across varying parameter sets.

    How to work through it

    1. Install Python with SymPy and SciPy or R with relevant calculus packages
    2. Define symbolic variables for prices, income, and CES parameters
    3. Construct the Lagrangian symbolically and solve the system of equations
    4. Implement a numerical optimizer to solve the same problem and plot indifference curves against the budget line
2

Core Microeconomics — Consumer Theory, Producer Theory & Equilibrium

Microeconomics studies how individual agents allocate scarce resources. This phase builds formal consumer theory, producer theory, perfect competition, and partial equilibrium welfare economics.

  • Derive consumer demand and indirect utility
    ~6hLearn1 resource

    Consumer theory provides the behavioral foundation for demand curves and welfare measurements such as consumer surplus.

    You'll learn

    • Marshallian Demand — uncompensated consumer demand expressed as a function of prices and nominal income
    • Hicksian Demand — compensated demand holding utility constant to isolate pure price substitution
    • Slutsky Equation — a decomposition showing how total price response equals substitution effect plus income effect
    • Roy's Identity — a method to recover Marshallian demand directly from the indirect utility function

    Study consumer preference axioms, utility functions, marginal rate of substitution (MRS), Marshallian demand, and Hicksian (compensated) demand. Use duality theory to link the Utility Maximization Problem (UMP) and Expenditure Minimization Problem (EMP) via Roy's Identity and the Slutsky equation.

    Done when: you can analytically derive Marshallian and Hicksian demand functions for a Cobb-Douglas utility function and decompose a price change into income and substitution effects.

    How to work through it

    1. State standard preference axioms (completeness, transitivity, monotonicity, convexity)
    2. Formulate and solve the Utility Maximization Problem to obtain Marshallian demand
    3. Formulate the Expenditure Minimization Problem to obtain the Expenditure function and Hicksian demand
    4. Derive the Slutsky equation to isolate substitution and income effects
  • Model production, cost minimization, and firm supply
    ~5hLearn

    Producer theory establishes how technological constraints and input costs translate into firm output decisions and market supply.

    You'll learn

    • MRTS (Marginal Rate of Technical Substitution) — the rate at which capital can substitute for labor while keeping output constant
    • Shephard's Lemma — a result showing that the derivative of the cost function with respect to input price yields conditional factor demand
    • Returns to Scale — the quantitative change in output when all inputs are increased by a common factor

    Analyse production functions, marginal product, returns to scale, and the Marginal Rate of Technical Substitution (MRTS). Solve the firm's cost-minimisation problem to derive short-run and long-run cost curves, followed by profit maximization to determine competitive supply curves.

    Done when: you can derive short-run and long-run total, average, and marginal cost curves from a two-factor production function.

    How to work through it

    1. Calculate marginal product of labour/capital and evaluate returns to scale
    2. Solve the firm's cost-minimization problem subject to an output target
    3. Derive conditional factor demands using Shephard's Lemma
    4. Set price equal to marginal cost to derive competitive short-run and long-run firm supply curves
  • Analyse partial equilibrium and tax incidence
    ~5hPractice

    Equilibrium and elasticity concepts form the standard baseline for real-world policy evaluation in government and industry.

    You'll learn

    • Tax Incidence — the distribution of tax burden between buyers and sellers governed by relative elasticities
    • Deadweight Loss — the net loss of total economic surplus caused by market distortions or interventions
    • Elasticity of Demand — the percentage change in quantity demanded in response to a one percent change in price

    Combine market demand and aggregate supply to solve for market equilibrium. Calculate consumer surplus, producer surplus, and deadweight loss. Examine the welfare consequences and economic incidence of specific taxes, subsidies, price ceilings, and price floors under different price elasticities.

    Done when: you can calculate the algebraic deadweight loss and the exact tax burden split between consumers and producers given explicit demand and supply elasticity parameters.

    How to work through it

    1. Aggregate individual demand and supply curves into market-level equivalents
    2. Calculate equilibrium price and quantity algebraically
    3. Compute own-price elasticity of demand and supply at equilibrium
    4. Introduce a per-unit tax, calculate the new equilibrium, tax revenue, and deadweight loss
  • Simulate partial equilibrium market interventions in code
    ~4hBuild

    Demonstrates practical ability to translate analytical microeconomic models into automated quantitative tools.

    You'll learn

    • Definite Integration in Code — computing areas under functions using numerical integration (scipy.integrate or stats::integrate)
    • ggplot2 / Matplotlib — standard visualization libraries for producing publication-quality economic figures

    Write a program in Python or R that takes supply and demand parameter functions, calculates market equilibrium, simulates market interventions (subsidies, quotas, price controls), and renders an annotated welfare diagram displaying deadweight loss and surplus transfers.

    Done when: the program outputs a clean chart and summary table quantifying surplus shifts and deadweight loss for any user-entered tax rate.

    How to work through it

    1. Create reusable classes or functions for linear and constant-elasticity supply/demand curves
    2. Implement root-finding or algebraic solvers to determine equilibrium with taxes or price ceilings
    3. Integrate under curves to compute numerical values for consumer and producer surplus
    4. Plot demand, supply, post-tax supply, and shaded surplus/deadweight loss regions using Matplotlib or ggplot2
3

Core Macroeconomics — Aggregate Output, Fluctuations & Policy

Macroeconomics analyses economies in aggregate. This phase covers national income accounting, short-run goods and money market equilibrium, medium-run aggregate supply/demand, and monetary and fiscal policy transmission mechanisms.

  • Calculate and evaluate national income accounts
    ~4hLearn1 resource

    All macroeconomic models and policy evaluations depend on standard national income accounting definitions.

    You'll learn

    • GDP Deflator — a measure of price inflation for all domestically produced goods and services in an economy
    • National Savings Identity — the equilibrium relation stating national savings must equal domestic investment plus net foreign investment
    • Balance of Payments — the systematic record of all economic transactions between residents of an economy and the rest of the world

    Study national income accounting identities: GDP (expenditure, income, and output approaches), GNP, real versus nominal GDP, deflators, and the consumer price index (CPI). Analyse trade balances, national savings, and investment identities in closed and open economies.

    Done when: you can reconcile balance of payments accounts and compute real GDP growth, chain-weighted deflators, and inflation rates from raw national accounts tables.

    How to work through it

    1. Review the GDP identity: Y = C + I + G + (X - M)
    2. Differentiate between chained volume measures and nominal GDP measures
    3. Calculate CPI and GDP deflator inflation metrics
    4. Formulate the national accounting identity linking private savings, government budget balance, and current account
  • Model short-run fluctuations using IS-LM and Mundell-Fleming
    ~6hLearn

    IS-LM remains the foundational conceptual bridge for understanding how monetary and fiscal policies interact with output and interest rates.

    You'll learn

    • IS Curve — combinations of interest rates and output levels where planned expenditure equals total output
    • Mundell-Fleming Model — open economy extension showing monetary/fiscal policy effectiveness depends on exchange rate regime
    • Monetary Transmission Mechanism — the channels through which central bank policy actions affect aggregate demand

    Derive the goods market equilibrium (IS curve) and money/financial market equilibrium (LM curve). Integrate the central bank interest rate rule (IS-MP framework) and extend to the open economy via the Mundell-Fleming model under fixed and floating exchange rates.

    Done when: you can solve an algebraic IS-LM/IS-MP model and determine the precise multiplier effects of a fiscal expansion under floating versus fixed exchange rate regimes.

    How to work through it

    1. Derive the Keynesian cross multiplier and the IS curve from goods market equilibrium
    2. Derive the LM curve from liquidity preference or the MP curve from an explicit monetary policy rule
    3. Solve for equilibrium output and interest rates simultaneously
    4. Extend the model with capital mobility to evaluate policy effectiveness under flexible vs fixed exchange rates
  • Analyse the Phillips Curve and medium-run AS-AD equilibrium
    ~5hPractice

    Explains the trade-offs facing central banks when balancing inflation targets against employment stability.

    You'll learn

    • NAIRU — Non-Accelerating Inflation Rate of Unemployment, the benchmark rate consistent with stable inflation
    • Expectations-Augmented Phillips Curve — inflation relationship accounting for expected inflation shifts
    • Sacrifice Ratio — the percentage of one year's GDP that must be foregone to reduce inflation by one percentage point

    Examine the evolution of the Phillips curve from the original empirical wage relation to the expectations-augmented and New Keynesian Phillips Curves. Combine aggregate supply and aggregate demand (AS-AD) to understand how supply shocks and demand shocks create output gaps and inflationary pressures.

    Done when: you can calculate the sacrifice ratio and solve for dynamic inflation and output trajectories following an unexpected adverse supply shock.

    How to work through it

    1. Derive the price-setting and wage-setting equations that determine the natural rate of unemployment (NAIRU)
    2. Derive the expectations-augmented Phillips Curve
    3. Solve the medium-run equilibrium where actual output converges to potential output
    4. Evaluate adaptive versus rational expectations and how credibility alters disinflation costs
  • Build an interactive macroeconomic policy simulator
    ~5hBuild

    Transforms abstract macroeconomic identities into an interactive computational framework used in policy simulation.

    You'll learn

    • Taylor Rule — an empirical monetary-policy rule prescribing how central banks should adjust nominal rates in response to inflation and output gaps
    • Impulse Response — the simulated time path of economic variables reacting to an initial exogenous shock

    Construct a Python or R script that simulates dynamic aggregate demand/supply responses to central bank interest rate shocks and fiscal stimulus packages using standard parameters, outputting impulse response trajectories for GDP, inflation, and public debt.

    Done when: the simulator reproduces standard monetary policy tightening dynamics and outputs dynamic path charts for inflation, real output gap, and the debt-to-GDP ratio over a 20-quarter horizon.

    How to work through it

    1. Define difference equations for the IS curve, Phillips curve, and Taylor rule
    2. Initialize shock vectors representing a 100-basis-point policy rate hike
    3. Iterate state variables forward through time for 20 periods
    4. Plot the simulated dynamic response curves and verify convergence back to steady state
4

Statistical Foundations & Applied Econometrics I (OLS & Inference)

Econometrics combines probability theory, statistical inference, and economic models to test hypotheses and estimate empirical relationships. This phase establishes probability fundamentals, properties of estimators, and Ordinary Least Squares (OLS) in R and Python.

  • Master probability theory and sampling distributions
    ~5hLearn1 resource

    Statistical inference is the theoretical bedrock of econometric testing and sample-to-population generalizations.

    You'll learn

    • Central Limit Theorem (CLT) — the theorem establishing that the sum of independent random variables tends toward a normal distribution
    • Law of Large Numbers (LLN) — the theorem stating that sample averages converge in probability to the expected value as sample size grows
    • Type I and Type II Errors — the statistical errors of rejecting a true null hypothesis versus failing to reject a false one

    Study random variables, joint, marginal, and conditional probability distributions, expectations, variance, covariance, and correlation. Learn the Law of Large Numbers (LLN) and Central Limit Theorem (CLT), and conduct classical hypothesis testing (t-tests, F-tests, p-values, confidence intervals).

    Done when: you can state and prove the properties of sample means as unbiased estimators and construct two-sided confidence intervals from summary sample statistics.

    How to work through it

    1. Review discrete and continuous probability density functions (Normal, t, Chi-square, F)
    2. Compute expected values and variances using linear expectation operators
    3. Formulate null and alternative hypotheses for population means
    4. Derive t-statistics and compute p-values for one-sample and two-sample hypothesis tests
  • Derive Ordinary Least Squares (OLS) and Gauss-Markov assumptions
    ~6hLearn1 resource

    Understanding the mathematical assumptions behind OLS is essential for identifying when and why OLS estimates break down in real applications.

    You'll learn

    • Gauss-Markov Theorem — mathematical proof that OLS has the minimum variance among all linear unbiased estimators
    • Strict Exogeneity — the assumption that error terms have conditional expectation zero given the regressors: E[u|X] = 0
    • Variance-Covariance Matrix of Estimators — a matrix displaying variances of estimated coefficients along the diagonal and covariances off-diagonal

    Derive the bivariate and multivariable OLS estimator analytically and in matrix notation: $\hat{\beta} = (X'X)^{-1}X'Y$. Prove the Gauss-Markov theorem showing OLS is the Best Linear Unbiased Estimator (BLUE) under classical assumptions.

    Done when: you can write the full algebraic proof that $E[\hat{\beta}] = \beta$ when $E[u|X] = 0$ and derive the variance-covariance matrix of the OLS estimator.

    How to work through it

    1. Set up the sum of squared residuals minimization problem
    2. Differentiate with respect to beta vectors to derive the normal equations
    3. Solve for beta hat in matrix notation
    4. List and interpret each Gauss-Markov assumption (linearity, strict exogeneity, no multicollinearity, spherical errors)
  • Diagnose and correct violations of OLS assumptions
    ~5hPractice

    Applied economic data almost always violates classical spherical error assumptions, requiring robust inference.

    You'll learn

    • Heteroskedasticity — condition where the variance of error terms varies across observations
    • Huber-White Robust Standard Errors — standard errors calculated via a sandwich variance estimator that remains consistent under heteroskedasticity
    • Variance Inflation Factor (VIF) — a diagnostic metric measuring the degree of collinearity among regressors

    Analyse what happens when classical assumptions fail: heteroskedasticity (non-constant variance), autocorrelation (correlated errors across time), and multicollinearity. Learn diagnostic tests (Breusch-Pagan, White, Durbin-Watson) and implement corrections using Robust Standard Errors (Huber-White/sandwich estimators).

    Done when: you can run regression diagnostics in R/Python, identify heteroskedasticity from residual plots and statistical tests, and report correct heteroskedasticity-robust (HC1/HC3) standard errors.

    How to work through it

    1. Inspect residual versus fitted plots to identify non-constant error variance
    2. Conduct the Breusch-Pagan and White tests for heteroskedasticity
    3. Estimate model parameters and replace standard error matrices with Huber-White sandwich estimators
    4. Compare uncorrected vs robust t-statistics and evaluate confidence interval changes
  • Estimate and report a wage equation (Mincer equation) in R or Python
    ~5hBuild1 resource

    Produces the first standard empirical piece expected in economic research and policy analysis.

    You'll learn

    • Mincer Equation — empirical model estimating the relationship between earnings, schooling, and labor market experience
    • Log-Linear Specification — regression form where coefficients are interpreted as percentage changes in the dependent variable
    • modelsummary / stargazer — packages for generating standard academic regression tables

    Obtain public microdata (e.g., US Current Population Survey or UK Labour Force Survey). Estimate a classic Mincer earnings model regresssing log wages on schooling, potential experience, and quadratic experience. Test joint hypotheses via F-tests and format results into a publication-ready regression table using stargazer or modelsummary.

    Done when: you have produced a reproducible script and formatted LaTeX/HTML regression table reporting estimated returns to education with standard and robust standard errors.

    How to work through it

    1. Download and clean a sample of individual-level wage microdata
    2. Construct log wage, potential experience, and experience squared variables
    3. Fit the linear regression model using R's lm() or Python's statsmodels
    4. Format the output table comparing unadjusted, multi-covariate, and robust SE specifications side-by-side
5

Econometrics II — Modern Causal Inference & Quasi-Experiments

Correlation is not causation. Modern applied microeconomics centres on design-based causal inference to evaluate policy interventions and counterfactuals. This phase covers the Potential Outcomes framework, Instrumental Variables, Difference-in-Differences, and Regression Discontinuity.

  • Understand the Potential Outcomes Framework and Endogeneity
    ~5hLearn1 resource

    The potential outcomes framework clarifies why simple regression comparisons fail and what conditions are needed for causal claims.

    You'll learn

    • Rubin Causal Model — a mathematical formulation defining causality in terms of potential outcomes for the same unit under treatment vs control
    • Omitted Variable Bias (OVB) — bias occurring when a statistical model leaves out one or more relevant confounding variables
    • Attenuation Bias — the biasing of an estimated coefficient toward zero caused by measurement error in an explanatory variable

    Study the Rubin Causal Model, Average Treatment Effect (ATE), Average Treatment Effect on the Treated (ATT), and selection bias. Understand the three sources of endogeneity: omitted variable bias (OVB), simultaneous causality, and measurement error.

    Done when: you can mathematically derive the omitted variable bias formula and prove how selection bias separates observational differences in means from true causal treatment effects.

    How to work through it

    1. Define counterfactual outcomes Y(1) and Y(0) and the fundamental problem of causal inference
    2. Decompose observational differences in group means into ATT and Selection Bias
    3. Derive the omitted variable bias formula: Short Beta = Long Beta + (Omitted Beta * Auxiliary Regressor)
    4. Identify the consequences of classical measurement error (attenuation bias)
  • Implement Instrumental Variables (IV) and Two-Stage Least Squares (2SLS)
    ~6hPractice1 resource

    IV is a primary tool for estimating causal parameters in the presence of unobserved confounders or reverse causality.

    You'll learn

    • Two-Stage Least Squares (2SLS) — regression technique estimating fitted values in stage 1 to replace endogenous variables in stage 2
    • Exclusion Restriction — assumption that an instrument affects the outcome only through the endogenous treatment
    • Local Average Treatment Effect (LATE) — the average treatment effect for the subpopulation of compliers induced to take treatment by the instrument

    Learn how instrumental variables solve endogeneity when $Cov(X, u) \neq 0$. Master the instrumental validity requirements: Instrument Relevance ($Cov(Z, X) \neq 0$) and Instrument Exogeneity ($Cov(Z, u) = 0$). Derive and execute Two-Stage Least Squares (2SLS), and interpret the Local Average Treatment Effect (LATE).

    Done when: you can derive the IV estimator in matrix form, check for weak instruments using first-stage F-statistics, and estimate a 2SLS model in R/Python/Stata using fixest::feols or linearmodels.

    How to work through it

    1. State the exclusion restriction and relevance conditions formally
    2. Derive the Wald estimator and the multivariable 2SLS estimator
    3. Evaluate first-stage F-statistics against the Stock-Yogo weak instrument thresholds
    4. Estimate a 2SLS model on an empirical dataset (e.g. Card's proximity-to-college data or Angrist-Krueger quarter-of-birth data)
  • Execute Difference-in-Differences (DiD) and Event Studies
    ~6hPractice

    Difference-in-differences is the most common empirical strategy used in empirical economics and policy evaluation.

    You'll learn

    • Parallel Trends Assumption — the identifying assumption that treated and control units would have followed identical outcome trajectories absent the intervention
    • Two-Way Fixed Effects (TWFE) — a panel regression controlling for all time-invariant unit characteristics and common time shocks
    • Clustered Standard Errors — standard errors adjusted for arbitrary within-group correlation of errors over time

    Master the Difference-in-Differences design for natural experiments. Understand the parallel trends assumption, two-way fixed effects (TWFE), event study leads and lags for pre-trend validation, and recent econometric advances regarding staggered treatment timing.

    Done when: you can construct an event-study plot testing for parallel pre-trends and estimate a generalized TWFE DiD model with clustered standard errors at the treatment unit level.

    How to work through it

    1. Derive the 2x2 DiD estimator as $(Y_{T, post} - Y_{T, pre}) - (Y_{C, post} - Y_{C, pre})$
    2. Specify the panel two-way fixed effects regression with unit and time fixed effects
    3. Create lead and lag indicators around treatment timing to generate an event-study specification
    4. Estimate the model with clustered standard errors and interpret the event-study graph for pre-trend violations
  • Estimate Regression Discontinuity Designs (RDD)
    ~5hPractice1 resource

    RDD yields causal estimates with internal validity approaching randomized controlled trials when institutional cutoffs exist.

    You'll learn

    • Running Variable (Forcing Variable) — the continuous score determining assignment to treatment around a fixed cutoff
    • rdrobust — the standard econometric package for robust bias-corrected local polynomial RDD estimation
    • McCrary Density Test — a diagnostic test checking for continuity of the running variable density at the threshold

    Study sharp and fuzzy Regression Discontinuity Designs (RDD). Understand the running variable, the cut-off threshold, bandwidth selection, local polynomial estimation, and validity checks (McCrary density test for manipulation, covariate balance tests).

    Done when: you can estimate a local linear RDD in R using rdrobust (or Python equivalent), produce an RDD discontinuity plot with binned scatter points, and verify lack of sorting around the cutoff.

    How to work through it

    1. Distinguish between Sharp RDD (treatment probability jumps from 0 to 1) and Fuzzy RDD (probability shifts discontinuously)
    2. Select optimal bandwidths using mean squared error (MSE) minimization criteria
    3. Estimate local linear regressions on either side of the cutoff
    4. Execute the McCrary density test to verify agents are not manipulating their position around the threshold
  • Replicate a published causal inference paper on minimum wage or education policy
    ~7hBuild

    Replication of published empirical work is the primary training method for professional applied economists.

    You'll learn

    • Replication Workflow — best practices for structuring data, cleaning scripts, and estimation routines for full reproducibility
    • Placebo Test — robustness check applying the econometric model to fake treatment dates or unaffected control groups

    Replicate the empirical findings of a landmark natural experiment paper (e.g. Card & Krueger 1994 on minimum wage, or an education policy reform dataset). Re-estimate baseline OLS, the core quasi-experimental specification (DiD or RDD), and run robustness tests (alternative bandwidths, placebo tests, and clustered standard errors).

    Done when: you produce a reproducible script and markdown report containing exact replication tables, event-study graphs, and written interpretations of the identifying assumptions.

    How to work through it

    1. Download original replication dataset from an open repository (such as Harvard Dataverse or ICPSR)
    2. Clean and process the raw data matching the authors' sample restrictions
    3. Re-estimate baseline summary statistics and main causal treatment regression specifications
    4. Compile an automated report with tables and figures matching the original paper's exhibits
6

Intermediate & Advanced Microeconomics — Game Theory & Information

Strategic interactions and imperfect information depart from standard competitive market assumptions. This phase covers non-cooperative game theory, asymmetric information (adverse selection, moral hazard), and basic mechanism design.

  • Solve static and dynamic games with complete information
    ~6hLearn1 resource

    Game theory provides the mathematical language for industrial organization, auction design, and strategic corporate decisions.

    You'll learn

    • Nash Equilibrium — an action profile where no player has a unilateral incentive to deviate from their chosen strategy
    • Subgame Perfection — a refinement requiring strategies to constitute a Nash equilibrium in every subgame of an extensive game
    • Backward Induction — solving a sequential game by analyzing the terminal nodes and working backwards

    Learn simultaneous games in normal form: dominated strategies, iterated elimination of strictly dominated strategies (IESDS), and Pure/Mixed Strategy Nash Equilibrium. Move to extensive form dynamic games: game trees, information sets, subgame perfection, and backward induction.

    Done when: you can solve for all pure and mixed strategy Nash equilibria in a 2x2 normal form game and find the Subgame Perfect Nash Equilibrium (SPNE) in a Stackelberg duopoly game.

    How to work through it

    1. Construct payoff matrices for normal-form games and identify best-response functions
    2. Calculate mixed strategy probabilities that make opponents indifferent
    3. Draw extensive-form game trees with decision nodes and information sets
    4. Apply backward induction to find Subgame Perfect Nash Equilibria in sequential games
  • Model asymmetric information: Adverse Selection, Signalling, and Moral Hazard
    ~6hLearn

    Asymmetric information explains real-world phenomena in insurance markets, credit markets, corporate executive pay, and labor contracts.

    You'll learn

    • Adverse Selection — market situation where unobserved asymmetric attributes lead to the disproportionate selection of undesirable agents
    • Moral Hazard — opportunistic change in behavior after entering a contract due to unobservable actions
    • Incentive Compatibility Constraint — constraint ensuring that an agent finds it in their private interest to choose the action desired by the principal

    Study market failures arising from private information. Cover adverse selection (Akerlof's Market for Lemons), Spence's labour market signalling model (separating vs pooling equilibria), and principal-agent moral hazard models with incentive compatibility and participation constraints.

    Done when: you can solve an analytical principal-agent model for the optimal incentive contract under observable vs unobservable effort.

    How to work through it

    1. Derive the adverse selection price unraveling condition in a used-goods market
    2. Set up the Spence job-market signalling model and derive separating equilibrium conditions
    3. Formulate the principal-agent optimization problem with Participation (Individual Rationality) constraints
    4. Add the Incentive Compatibility (IC) constraint and solve for optimal wage schedules
  • Analyse monopoly pricing, price discrimination, and oligopoly models
    ~5hPractice

    These models form the backbone of antitrust economics, competition policy, and private-sector pricing strategy.

    You'll learn

    • Lerner Index — a measure of a firm's market power calculated as (P - MC) / P
    • Cournot Competition — an oligopoly model where firms simultaneously choose quantities to produce
    • Bertrand Paradox — the result showing that two firms competing on price with identical marginal costs drive prices down to marginal cost

    Examine firm behavior under imperfect competition. Model first, second, and third-degree price discrimination, two-part tariffs, and standard oligopoly models: Cournot (quantity competition), Bertrand (price competition with homogeneous and differentiated goods), and Hotelling's spatial model.

    Done when: you can derive equilibrium prices, quantities, and deadweight loss across Cournot, Bertrand, and Collusion scenarios for identical market demand functions.

    How to work through it

    1. Solve the profit-maximization problem for a single-price monopolist and calculate the Lerner Index
    2. Derive first, second, and third-degree price discrimination solutions
    3. Solve the Cournot duopoly simultaneous reaction functions and find equilibrium quantities
    4. Solve the Bertrand price competition paradox with homogeneous goods vs product differentiation
  • Build a numerical Cournot-Bertrand oligopoly market simulation
    ~5hBuild

    Directly simulates the market screening tools used by competition authorities (FTC, CMA, DG-COMP) and economic consultancies.

    You'll learn

    • Herfindahl-Hirschman Index (HHI) — a standard metric of market concentration calculated by summing squared market shares of all firms
    • Reaction Function — a function giving a firm's optimal action as a function of competitors' chosen actions

    Write a Python or R script that simulates an N-firm oligopoly. Allow users to configure marginal costs, demand elasticity, and degree of product differentiation, then compare market outcomes (price, consumer surplus, industry profits, concentration indices like HHI) across Cournot, Bertrand, and Cartel regimes.

    Done when: the script computes numerical equilibria and outputs an comparative analysis chart showing market power and Herfindahl-Hirschman Index (HHI) changes as the number of firms varies from 1 to 10.

    How to work through it

    1. Define market demand systems with differentiated goods substitution parameters
    2. Implement reaction function iteration to find Cournot and Bertrand equilibrium prices and quantities
    3. Calculate the Herfindahl-Hirschman Index (HHI) for each scenario
    4. Plot market price markups and deadweight loss as firm count N varies from 1 to 10
7

Intermediate & Advanced Macroeconomics — Dynamic Models & Growth Theory

Modern macroeconomics models dynamic choices over time and stochastic uncertainty. This phase covers long-run economic growth (Solow-Swan, Romer), intertemporal consumption choices, and foundations of Dynamic Stochastic General Equilibrium (DSGE) models.

  • Derive and simulate the Solow-Swan and Endogenous Growth Models
    ~5hLearn

    Long-run growth determines living standards and structural economic performance across countries.

    You'll learn

    • Solow Residual — the portion of economic growth unexplained by capital and labor accumulation, attributed to technological progress (TFP)
    • Golden Rule Savings Rate — the savings rate that maximizes steady-state consumption per capita
    • Non-Rivalry of Ideas — economic property where technological knowledge can be used simultaneously by infinite producers without depletion

    Study the Solow-Swan neoclassical growth model: capital accumulation, steady-state capital per worker, the Golden Rule savings rate, and growth accounting. Transition to endogenous growth (Romer model of ideas and R&D) where non-rivalry creates sustained positive growth.

    Done when: you can derive the steady-state equation for capital per effective worker analytically and calculate total factor productivity (TFP) growth via Solow residual decomposition.

    How to work through it

    1. Set up the capital accumulation differential equation: dk/dt = s*f(k) - (n + g + delta)*k
    2. Solve for the steady-state capital-labor ratio and output per capita
    3. Derive the Golden Rule capital stock maximizing steady-state consumption
    4. Perform growth accounting to decompose historical GDP growth into capital, labor, and Total Factor Productivity (Solow residual)
  • Formulate intertemporal consumption: Ramsey-Cass-Koopmans and Euler Equations
    ~6hLearn

    The Consumption Euler equation is the core microfoundation used across all modern dynamic macroeconomic models.

    You'll learn

    • Consumption Euler Equation — intertemporal optimality condition relating marginal utility today to discounted marginal utility tomorrow
    • Hamiltonian Function — a mathematical optimization tool used for continuous-time optimal control problems
    • Co-State Variable — the dynamic equivalent of a Lagrange multiplier representing the marginal value of the state variable (capital)

    Move beyond exogenous savings rates to dynamic consumer optimization over infinite horizons. Solve the Ramsey-Cass-Koopmans model using the Hamiltonian (or dynamic programming Bellman equation) to derive the Consumption Euler Equation.

    Done when: you can set up the dynamic optimization Hamiltonian and derive the Euler equation showing optimal consumption growth as a function of the real interest rate and subjective discount rate.

    How to work through it

    1. Set up the infinite-horizon intertemporal utility maximization problem
    2. Construct the Current-Value Hamiltonian with co-state variables
    3. Derive first-order conditions with respect to consumption and capital
    4. Interpret the Euler equation: consumption growth rate = (r - rho) / theta
  • Understand Real Business Cycle (RBC) and New Keynesian DSGE frameworks
    ~6hLearn1 resource

    DSGE models are the primary analytical tools used by central banks (Fed, ECB, Bank of England) and the IMF for macroeconomic forecasting and monetary policy counterfactuals.

    You'll learn

    • DSGE Model — Dynamic Stochastic General Equilibrium model combining microfoundations, rational expectations, and aggregate shocks
    • Calvo Pricing — standard theoretical mechanism where only a random fraction of firms can reset prices in each period
    • Log-Linearization — mathematical approximation technique converting non-linear dynamic systems into linear equations around a steady state

    Explore the foundations of Dynamic Stochastic General Equilibrium (DSGE) models. Compare Real Business Cycle theory (fluctuations driven by technological productivity shocks with flexible prices) with New Keynesian models (monetary non-neutrality arising from Calvo sticky prices and monopolistic competition).

    Done when: you can explain the core three-equation New Keynesian model (Dynamic IS curve, New Keynesian Phillips Curve, and Monetary Policy Rule) and contrast its policy implications with RBC models.

    How to work through it

    1. Study the household, firm, and market clearing conditions in a baseline RBC model
    2. Examine how Calvo price stickiness creates price rigidities and real effects from monetary policy
    3. Derive the log-linearized three-equation New Keynesian model
    4. Examine how policy should respond to supply shocks versus demand shocks
  • Simulate a 3-Equation New Keynesian dynamic macroeconomic model in Python
    ~6hBuild

    Proves technical competency in numerical macroeconomic methods, bridging theoretical macroeconomics and computational macro-modelling.

    You'll learn

    • Impulse Response Function (IRF) — dynamic trajectory tracing how endogenous macro variables respond across subsequent quarters to an exogenous shock
    • Rational Expectations Solver — numerical method (such as Blanchard-Kahn or Klein algorithm) for solving dynamic systems with forward-looking expectations

    Write a script that solves a log-linearized three-equation New Keynesian model using matrix reduction methods. Simulate macroeconomic responses (output gap, inflation, policy rate) to monetary policy shocks and cost-push supply shocks over 40 periods.

    Done when: the notebook computes impulse response functions (IRFs) matching standard central bank benchmark paths following a positive inflation shock.

    How to work through it

    1. Define the structural matrices for the Dynamic IS, NK Phillips Curve, and Taylor Rule equations
    2. Solve for the recursive law of motion using standard linear rational expectations solvers
    3. Generate impulse response vectors for interest rate and productivity shocks
    4. Plot and annotate multi-panel dynamic impulse response graphs
8

Empirical Economics & Professional Data Workflows (Stata, R, Python)

Professional economists must manage large microdata and time-series pipelines, ensure full computational reproducibility, and work across R, Python, and Stata. This phase establishes industry-standard data engineering, version control, and replication workflows.

  • Manage and clean large economic microdata pipelines
    ~5hPractice1 resource

    Over 70% of empirical research time in policy and consulting is spent cleaning raw survey and administrative datasets.

    You'll learn

    • Survey Weights (Probability Weights) — sampling multipliers applied to observations to ensure sample statistics represent the true population
    • Panel Data Reshaping — restructuring datasets between long format (unit-time indexed rows) and wide format (time-indexed columns)
    • Polars / data.table — high-performance data manipulation engines capable of handling multi-gigabyte economic datasets

    Learn efficient data wrangling for complex survey microdata (IPUMS, ACS, LFS, or World Bank microdata). Clean missing values, handle survey weights, construct panel indices, reshape between wide and long formats, and merge disparate administrative datasets using data.table/dplyr in R or pandas/polars in Python.

    Done when: you write a clean, automated script that ingests a multi-year raw survey dataset, applies survey weighting, reshapes panel structures, and outputs a clean analysis-ready dataset without manual editing.

    How to work through it

    1. Download raw microdata and study codebooks and survey sampling weights
    2. Filter, clean, and recode categorical variables and handle missing value codes
    3. Reshape data from wide (repeated cross-sections) to long (panel) format
    4. Validate merged datasets using primary and foreign key integrity checks
  • Translate and cross-validate econometric models across Stata, R, and Python
    ~5hPractice1 resource

    Eliminates tool-specific friction, allowing you to collaborate seamlessly across academia, government, and private-sector teams regardless of their software choice.

    You'll learn

    • Stata do-file — batch command script used across standard academic and institutional economics workflows
    • fixest — ultra-fast R package for high-dimensional fixed-effects regressions
    • linearmodels — Python library providing econometric panel data and instrumental variable estimators

    Much of applied economics and legacy institutional code in central banks and think tanks is written in Stata, while newer pipelines use R and Python. Write identical econometric routines (Fixed Effects and 2SLS) across Stata do-files, R scripts (fixest), and Python (linearmodels), verifying that coefficients, standard errors, and clustering match to four decimal places.

    Done when: you have created a cross-language comparison repository demonstrating identical regression outputs across Stata, R, and Python on a shared panel dataset.

    How to work through it

    1. Create a standardized panel test dataset with unit, time, and cluster identifiers
    2. Write an R script using `feols()` from the `fixest` package with two-way fixed effects and clustered SEs
    3. Write a Python script using `linearmodels.PanelOLS` with identical specifications
    4. Write an equivalent Stata do-file using `reghdfe` and verify point estimates and standard errors match exactly
  • Implement professional version control and reproducible research workflows
    ~5hBuild

    Leading economic journals, policy agencies, and consultancies require end-to-end reproducible pipelines for quality control.

    You'll learn

    • Quarto — scientific publishing system for weaving computational code, LaTeX equations, and narrative into publication documents
    • AEA Data and Code Availability Policy — the standard reproducibility benchmark in economics requiring turnkey replication packages
    • Makefile — an automation tool defining dependency chains and commands to compile data pipelines

    Learn reproducibility standards in modern economics (such as AEA Data and Code Availability requirements). Structure projects using Git, GitHub, dynamic documents (Quarto / R Markdown / Jupyter), and automated build runners (Makefiles) so that raw data transforms into final tables and PDF reports with a single command.

    Done when: you can build a complete GitHub repository containing a Makefile that executes data cleaning, runs regressions, and compiles a Quarto PDF working paper report from scratch without errors.

    How to work through it

    1. Set up a standardized economic project folder structure (data/raw, data/clean, src/, output/tables, output/figures)
    2. Initialize a Git repository and commit code iteratively with meaningful commit messages
    3. Create a Makefile automating script execution from data ingestion to document compilation
    4. Compile an automated PDF research report using Quarto containing inline econometric results and formatted tables
9

Specialised Domain Tracks (Policy, Central Banking & Private Sector)

Economics diverges into distinct applied domains. This phase explores the specific methodologies and questions of three major career routes: Public Policy / International Organizations, Central Banking / Macro Finance, and Economic Consulting / Industrial Strategy.

  • Track A (Public Policy): Conduct Cost-Benefit Analysis and Social Program Evaluation
    ~6hPractice1 resource

    Core methodology required for government treasury analysts, World Bank/OECD specialists, and policy think tanks.

    You'll learn

    • Social Discount Rate — the discount rate used by governments to evaluate the present value of future social costs and benefits
    • Value of a Statistical Life (VSL) — the monetary value placed on reducing the statistical risk of premature mortality
    • Pigouvian Tax — a tax levied on market activities that generate negative external costs on third parties

    Study public economics methods: social welfare functions, public goods provision, externalities, Pigouvian taxes, and practical Cost-Benefit Analysis (CBA). Learn to calculate Net Present Value (NPV), Social Discount Rates, and Value of a Statistical Life (VSL) for government infrastructure and social welfare programs.

    Done when: you produce a complete 5-page Cost-Benefit Analysis evaluation report for a simulated public transportation or healthcare initiative, including sensitivity analyses across alternative social discount rates.

    How to work through it

    1. Identify all direct, indirect, and external social costs and benefits of an intervention
    2. Monetize non-market benefits using stated/revealed preference metrics and Value of Statistical Life (VSL)
    3. Apply official government social discount rates to discount future cashflows and social surplus
    4. Calculate Net Present Value (NPV), Benefit-Cost Ratio (BCR), and conduct Monte Carlo sensitivity analysis
  • Track B (Central Banking): Estimate Time-Series Vector Autoregressions (VAR)
    ~6hPractice

    Vector Autoregressions are the standard empirical time series tool used across central banks and financial market macro desks.

    You'll learn

    • Stationarity — property of a time series where mean, variance, and autocovariance are invariant over time
    • Vector Autoregression (VAR) — an econometric model capturing dynamic interdependencies among multiple time series variables
    • Cholesky Identification — a causal identification scheme in SVARs imposing recursive triangular zero restrictions on contemporaneous relationships

    Study empirical time series econometrics: stationarity, unit root tests (Augmented Dickey-Fuller), cointegration, and Vector Autoregressions (VAR). Estimate a Structural VAR (SVAR) using Cholesky recursive identification or sign restrictions to measure the transmission of monetary policy shocks to inflation and output.

    Done when: you estimate an SVAR in R/Python on central bank macroeconomic data, produce structural impulse response functions (IRFs), and compute forecast error variance decompositions (FEVD).

    How to work through it

    1. Test macroeconomic time series for stationarity using ADF and KPSS tests and apply differencing if required
    2. Select optimal lag length using AIC/BIC information criteria
    3. Estimate a reduced-form VAR model and identify structural shocks using Cholesky decomposition
    4. Generate and interpret structural impulse response functions and variance decompositions
  • Track C (Consulting): Perform Merger Simulation and Market Definition Analysis
    ~6hPractice1 resource

    Directly matches the casework conducted in competition practices at economic consultancies (e.g. Compass Lexecon, Cornerstone, Analysis Group, Charles River Associates).

    You'll learn

    • SSNIP Test (Small but Significant Non-Transitory Increase in Price) — standard antitrust test defining the relevant antitrust market boundary
    • GUPPI (Gross Upward Pricing Pressure Index) — index measuring post-merger unilateral price increase incentives without requiring full demand estimation
    • Diversion Ratio — the proportion of sales lost by product A that capture product B following a price increase in A

    Learn quantitative techniques used in antitrust and economic consulting: market definition tests (Hypothetical Monopolist Test / SSNIP test), Critical Loss Analysis, Upward Pricing Pressure (UPP), and structural merger simulation with discrete choice demand (Logit / BLP frameworks).

    Done when: you write a market analysis report computing Gross Upward Pricing Pressure Index (GUPPI) and simulating post-merger price increases for a proposed corporate acquisition.

    How to work through it

    1. Review merger enforcement guidelines from competition authorities (FTC/DOJ or CMA/EC)
    2. Calculate diversion ratios between competing substitute products using elasticity matrices
    3. Compute Gross Upward Pricing Pressure Index (GUPPI) metrics to assess unilateral incentive to raise prices
    4. Simulate equilibrium post-merger price changes under Bertrand competition with differentiated goods
  • Synthesize a domain capstone analysis into a technical memorandum
    ~7hBuild

    Produces a capstone portfolio exhibit tailored to your target sector within the economics profession.

    You'll learn

    • Executive Economic Memorandum — concise professional report synthesizing complex quantitative findings for decision-makers
    • Sensitivity Analysis — testing model outcomes across wide parameter variations to prove robustness

    Select one domain track (Policy CBA, Central Bank Macro Forecasting, or Consulting Antitrust Case) and produce a full technical memorandum complete with theoretical framework, empirical estimation, sensitivity checks, and executive recommendations.

    Done when: you deliver a complete 8-to-10-page memorandum with structured executive summaries, econometric tables, annotated charts, and reproducible code appendices.

    How to work through it

    1. Select one track and formulate a specific analytical question and dataset
    2. Structure the memorandum: Executive Summary, Background, Theoretical Framework, Empirical Methodology, Results, Robustness, Policy Implications
    3. Execute the econometric and quantitative analysis using clean Python/R/Stata scripts
    4. Format tables and graphs and write concise, non-technical executive translations alongside technical appendices
10

Academic Working Papers, Policy Briefs & Research Dissemination

Economists communicate complex quantitative findings to technical peers, policymakers, and the public. This phase covers drafting an empirical working paper, writing concise policy briefs, and presenting economic arguments.

  • Draft a complete empirical working paper in academic format
    ~8hBuild

    An empirical working paper or writing sample is the core requirement for PhD applications, predoctoral fellowships, and academic/think-tank hiring.

    You'll learn

    • Academic Writing in Economics — structured writing style emphasizing explicit identifying assumptions and value-added contribution
    • LaTeX in Economics — standard typesetting language for rendering mathematical proofs and publication-quality tables

    Write a full working paper following standard economic journal structure: Introduction (hook, research question, contribution, roadmap), Institutional Context, Data, Identification Strategy, Main Results, Mechanisms, Robustness, and Conclusion. Format the paper in LaTeX using standard templates.

    Done when: you have written and compiled a complete 15-to-20 page empirical research paper in LaTeX with clean citations, mathematical models, and formatted regression exhibits.

    How to work through it

    1. Draft the introduction outlining the core causal question, identification strategy, and primary quantitative finding
    2. Detail the identification strategy, stating identifying assumptions and threats to validity
    3. Present main regression results alongside mechanisms and heterogeneous treatment effects
    4. Compile the paper in LaTeX using standard packages (amsmath, booktabs, hyperref)
  • Write an executive policy brief and non-technical summary
    ~4hPractice1 resource

    Senior civil servants, politicians, and corporate executives make decisions based on 2-page briefs, not 40-page technical papers.

    You'll learn

    • Policy Brief Format — concise executive document translating empirical econometric findings for decision-makers
    • VoxEU Style — standard format for accessible, high-impact economic research summaries

    Translate your technical working paper findings into a 2-page Policy Brief (in the style of VoxEU or NBER Digest). Focus on the policy problem, the empirical evidence, trade-offs, and actionable recommendations without relying on econometric jargon.

    Done when: you complete a 2-page executive brief containing a non-technical summary, two key visual charts, and bulleted policy recommendations designed for non-economist policymakers.

    How to work through it

    1. Extract the primary policy-relevant insight from your research paper
    2. Write a compelling 150-word executive summary stating the problem, finding, and recommendation
    3. Create two accessible summary figures illustrating the core findings without complex regression output
    4. Draft structured policy takeaways highlighting implementation costs, unintended consequences, and trade-offs
  • Present economic research in a slide deck and mock presentation
    ~4hApply

    Economist hiring across academia, central banks, and consultancies involves rigorous job talks where candidates defend their empirical identification under intense questioning.

    You'll learn

    • Economics Job Talk Slide Structure — presentation format emphasizing rapid preview of findings before methodology
    • LaTeX Beamer — presentation software widely used in economics departments and central banks

    Construct a 20-slide research deck following standard economics presentation conventions (motivation, preview of findings, model/identification, main tables, robustness). Deliver a recorded 15-minute mock presentation defending your identification strategy against potential audience objections.

    Done when: you have created the presentation slide deck and recorded a continuous 15-minute video walkthrough clearly explaining your theoretical motivation and empirical results.

    How to work through it

    1. Build slides using LaTeX Beamer or modern presentation tools with minimal text and clear table excerpts
    2. Include a preview slide summarizing main numerical estimates in the first 3 minutes
    3. Structure slides to address threats to validity (confounders, measurement error, attrition)
    4. Record and review your 15-minute presentation, timing each section accurately
11

Career Pathways, Technical Assessments & Professional Preparation

There is no single career path into economics. This phase prepares you for the distinct hiring processes and technical assessments used across PhD programs / Predocs, Government / Central Bank analyst schemes, and Private Sector / Consulting case interviews.

  • Prepare for Academic / Predoctoral Fellowships and PhD Applications
    ~4hApply1 resource

    Predoctoral fellowships have become the standard bridge into top Economics PhD programs for candidates building research credentials.

    You'll learn

    • Predoctoral Fellowship (Predoc) — a 1-to-2 year full-time research assistant position preparing candidates for economics PhD programs
    • Real Analysis — formal mathematical course in rigorous proofs often required for top-tier economics doctoral admissions

    Understand the academic pipeline (Research Assistant / Predoc -> PhD -> Assistant Professor / Researcher). Review standard PhD admissions requirements (advanced real analysis, linear algebra, multivariable calculus, strong GRE quant scores, and sample research papers). Assemble a portfolio highlighting clean coding skills, econometrics, and letters of recommendation strategy.

    Done when: you compile a complete predoc/PhD application packet including a curated GitHub portfolio, curriculum vitae (CV), statement of purpose, and an annotated writing sample.

    How to work through it

    1. Audit math prerequisites and identify any gaps in advanced real analysis or linear algebra
    2. Review successful Predoc and PhD statements of purpose from NBER/Predoc.org repositories
    3. Format an academic CV highlighting econometrics coursework, programming capabilities, and research papers
    4. Clean and link your GitHub working paper repository as a public coding sample
  • Drill Public Sector and Central Bank technical economics assessments
    ~5hPractice

    Central banks and government ministries use standardized written and technical economics assessments as their primary initial screening mechanism.

    You'll learn

    • Government Economic Service (GES) Competencies — standardized frameworks evaluating economic reasoning, policy analysis, and communication
    • Policy Trade-Off Matrix — structured analytical method weighing short-term costs versus long-term economic gains

    Practice the timed analytical tests, policy essay questions, and economic reasoning exams standard in central bank analyst programs (e.g. Bank of England, Federal Reserve, ECB) and government economic services (e.g. UK GES). Focus on answering unseen macroeconomic scenario questions and evaluating policy dilemmas under time constraints.

    Done when: you have completed three timed 60-minute practice assessments responding to macroeconomic shock scenarios and explaining the policy trade-offs in structured written essays.

    How to work through it

    1. Gather past sample questions from central bank and government economic service entry tests
    2. Practice structuring 45-minute essays analyzing an inflation surge or exchange rate crisis under strict time limits
    3. Apply the three-step framework: Theoretical Model -> Empirical Mechanism -> Policy Recommendation and Risks
    4. Review your answers against official government appraisal criteria (e.g. GES technical competencies)
  • Master Economic Consulting case interviews and quantitative take-home tests
    ~5hApply

    Economic consulting firms evaluate candidates through rigorous quantitative cases that test microeconomic intuition and econometric common sense.

    You'll learn

    • Cartel Overcharge Calculation — quantitative estimation of the price markup artificially imposed by collusive cartels
    • Economic Consulting Case Interview — specialized interview format evaluating microeconomic intuition, market definition, and data interpretation

    Practice consulting case interviews specific to economic consultancies. These differ from general management consulting cases by focusing on market power, quantitative estimation logic, antitrust damage calculations, and econometric problem-solving during live interviews or 48-hour take-home coding challenges.

    Done when: you have completed four mock economic consulting case interviews covering cartel overcharge damages, merger clearance, and econometric specification critique under live interview conditions.

    How to work through it

    1. Study standard economic consulting case frameworks (Mergers, Price-Fixing Damages, Patent Infringement, Regulatory Review)
    2. Practice estimating damages using 'Before-and-After' and 'Difference-in-Differences' benchmark methods during live case prompts
    3. Solve take-home econometric challenge datasets under 4-hour constraints, producing clean regression output and slide decks
    4. Conduct mock interviews with peers or mentors critiquing verbal articulation of economic concepts

How the plan fits together

11 phases in 7 stages. Anything on the same row can be worked on at the same time.

An arrow points from a phase to the work it unlocks: before starting any phase, every phase with an arrow into it has to be finished first.

STARTSTAGE 2STAGE 3STAGE 4STAGE 5STAGE 6STAGE 71Mathematical Foundationsfor Economic Analysis4 tasks · ~20h2Core Microeconomics —Consumer Theory, ProducerTheory & Equilibrium4 tasks · ~20h3Core Macroeconomics —Aggregate Output,Fluctuations & Policy4 tasks · ~20h4Statistical Foundations &Applied Econometrics I(OLS & Inference)4 tasks · ~21h5Econometrics II — ModernCausal Inference &Quasi-Experiments5 tasks · ~29h6Intermediate & AdvancedMicroeconomics — GameTheory & Information4 tasks · ~22h7Intermediate & AdvancedMacroeconomics — DynamicModels & Growth Theory4 tasks · ~23h8Empirical Economics &Professional DataWorkflows (Stata, R,…3 tasks · ~15h9Specialised Domain Tracks(Policy, Central Banking &Private Sector)4 tasks · ~25h10Academic Working Papers,Policy Briefs & ResearchDissemination3 tasks · ~16h11Career Pathways, TechnicalAssessments & ProfessionalPreparation3 tasks · ~14h
Solid arrow
Must be finished before the phase it points to
Dashed arrow
Same rule, but the prerequisite sits more than one stage back

Resources

22 in this plan's library, beyond the links on individual tasks.

Core Texts & Lectures

Foundational textbooks, open courseware, and lecture notes across economics.

  • 14.01SC Principles of Microeconomics

    Comprehensive microeconomic course covering consumer theory, producer theory, and market equilibrium with video lectures and problem sets.

    ocw.mit.edu · MIT OpenCourseWare (Massachusetts Institute of Technology) · MIT OpenCourseWare · Free (open access). · Introductory

  • Causal Inference: The Mixtape

    Applied microeconometrics guide detailing design-based causal inference methods including DAGs, IV, DiD, and RDD.

    mixtape.scunning.com · Yale University Press · Book · Free online open-access version; paid print edition published by Yale University Press ($35–$45). · Intermediate to Advanced

  • Code and Data for the Social Sciences: A Practitioner's Guide

    Practical manual outlining reproducible empirical research workflows, directory automation, defensive coding, and version control.

    web.stanford.edu · Matthew Gentzkow and Jesse M. Shapiro (Stanford University and Harvard/Brown University) · Guide · Free PDF. · Intermediate

  • ECON 159: Game Theory

    Rigorous lecture series covering non-cooperative game theory, Nash equilibrium, backward induction, and information economics.

    oyc.yale.edu · Open Yale Courses (Yale University) · Course · Free (open access). · Intermediate

  • Financial Programming and Policies, Part 1: Macroeconomic Accounts & Analysis (FPP.1x)

    Applied policy training in macroeconomic surveillance and integrated accounting across real, fiscal, monetary, and external sectors.

    International Monetary Fund (IMF) via edX · Online Course · Free to audit; optional paid verified certificate ($50–$100). · Intermediate

  • Game Theory: An Introduction

    Comprehensive guide to static and dynamic games of complete and incomplete information, adverse selection, and signaling models.

    Princeton University Press · Book · ~£45 · Intermediate-Advanced

  • Intermediate Microeconomics: A Modern Approach

    Essential core text covering formal utility maximization, cost minimization, competitive markets, and partial equilibrium welfare analysis.

    W. W. Norton & Company · Book · ~£60 · Intermediate

  • Introduction to Econometrics with R

    Hands-on companion to Stock and Watson that walks through OLS estimation, hypothesis testing, and specification tests with reproducible R code.

    econometrics-with-r.org · University of Duisburg-Essen · Interactive Book · Free · Beginner-Intermediate

  • Introductory Econometrics: A Modern Approach

    Standard foundational textbook establishing OLS mechanics, Gauss-Markov assumptions, hypothesis testing, and introductory panel data methods.

    cengage.com · Cengage Learning · Book · Paid textbook (approx. $75–$85 for digital rental/purchase; physical copies vary). · Intermediate

  • Macroeconomics

    A rigorous, intuitive treatment of IS-LM, aggregate demand/supply, monetary policy, and macroeconomic fluctuations across short and medium horizons.

    Pearson · Book · ~£55 · Intermediate

  • Mathematics for Economists

    Canonical reference textbook bridging pure mathematics and formal economic theory through linear algebra, calculus, and optimization.

    wwnorton.com · W. W. Norton & Company · Book · Paid textbook (approx. $80–$100 new, varies by edition/used format). · Intermediate

  • MIT OpenCourseWare: Economics Repository

    Repository of open courseware materials, lecture notes, and problem sets across undergraduate and graduate economics subjects.

    ocw.mit.edu · Massachusetts Institute of Technology (MIT) · MIT OpenCourseWare · Free. · Introductory to Advanced

  • QuantEcon: Advanced Quantitative Economics with Python

    Advanced computational curriculum formalizing dynamic programming, Bellman equations, Markov chains, and numerical methods for macroeconomics.

    python-advanced.quantecon.org · QuantEcon Project (Thomas J. Sargent and John Stachurski) · Online Course / Book · Free (open source / open access). · Advanced

  • QuantEcon: Quantitative Economics with Python

    Open-source lectures teaching dynamic programming, Markov chains, and computational macroeconomics using Python.

    quantecon.org · QuantEcon (Thomas J. Sargent & John Stachurski) · Open Courseware · Free · Advanced

  • The Economy 1.0 (and The Economy: Macroeconomics)

    Modern open-access textbook exploring aggregate fluctuations, the 3-Equation model, and monetary-fiscal policy transmission mechanisms.

    core-econ.org · Oxford University Press / CORE Econ Project · Book · Free online open-access textbook (print copy available for purchase). · Introductory to Intermediate

  • Writing Tips for Ph.D. Students

    Authoritative guide on structuring economics research papers, presenting empirical identification, and formatting regression results.

    John H. Cochrane (Stanford University / Hoover Institution) · Guide · Free PDF. · Advanced

Economic Microdata & Macro Portals

Public repositories for census data, survey microdata, and national accounts.

  • FRED (Federal Reserve Economic Data)

    Central macroeconomic data portal offering hundreds of thousands of downloadable time-series spanning national accounts, inflation, and interest rates.

    fred.stlouisfed.org · Federal Reserve Bank of St. Louis · Data Portal · Free. · All Levels

  • IPUMS

    Harmonized census and survey microdata platform across international and US surveys for empirical microeconomic analysis.

    ipums.org · Institute for Social Research and Data Innovation, University of Minnesota · Data Portal · Free for academic and research use (registration required). · Intermediate to Advanced

Computational & Econometric Tools

Software libraries and packages for R, Python, and Stata workflows.

  • fixest: Fast Fixed-Effects Estimations for R

    High-performance R package specialized for estimating multi-way fixed-effects regressions, instrumental variables, and event studies.

    lrberge.github.io · Laurent Bergé (Hosted on CRAN / GitHub) · Software / R Package · Free (Open-source / GPL-3). · Intermediate

Professional Societies & Networks

Academic associations, predoc networks, and economics discussion forums.

  • American Economic Association (AEA)

    Leading professional association for economists hosting premier journals, research resources, and the JOE academic job board.

    aeaweb.org · American Economic Association · Professional Association · Free website and job board access; student/professional memberships available for full conference and journal benefits. · All Levels

  • NBER Working Papers

    The leading working paper series to monitor cutting-edge frontier research across all subfields of economics and policy analysis.

    nber.org · National Bureau of Economic Research · Research Repository · Free abstracts / Open access via many academic institutions · Advanced

  • Pathways to Research and Doctoral Careers (PREDOC)

    Centralized portal providing listings, coding sample tasks, and assessment resources for pre-doctoral research positions.

    predoc.org · PREDOC Consortium (Consortium of leading universities and Federal Reserve research divisions) · Platform · Free. · Intermediate