economics phd

Everything you need to know about a PhD in economics

VoxDev Blog

Published 08.01.26
abdoulaye ndiaye

Abdoulaye Ndiaye

Assistant Professor of Economics, New York University

A set of answers to many of the most common questions about economics PhDs, including: What is an economics PhD? Should I do an economics PhD? And how should I prepare and apply to a PhD programme?

This note is for students and early-career professionals who want to know whether an economics PhD makes sense for them, and, if so, how to prepare and apply. It is written in English on purpose. Most PhD coursework, research seminars, and published economic papers are in English, even when your prior training may have been francophone, as with many students from Africa.

As there is evidence that for first-generation students, the Hidden Curriculum is real and costly, I hope this note can be of benefit to all those who reach out for advice or mentoring. It is tough to respond to the many requests I receive – so please share this widely!

The PhD in Economics: What it is, timeline and reality

What an economics PhD is in practice

A PhD is training to produce original research. It is not only ‘more classes’. It is a shift from learning tools to generating knowledge.

Many programmes have a similar skeleton:

  • Core microeconomics, macroeconomics, and econometrics.
  • Field courses (your specialisation).
  • Research papers leading to a dissertation.
  • A ‘job market paper’ (your flagship paper used to apply for jobs).

Typical programme structure (US vs Europe/UK)

United States (often 5-6 years)

  • Year 1: core sequence (micro/macro/metrics), qualifying exams (“prelims”).
  • Year 2: field courses, research exploration, RA/TA work.
  • Years 3-5/6: research papers, dissertation, job market preparation.

Europe/UK (often shorter after a strong masters)

  • Many paths start after a research-oriented masters.
  • The PhD can be closer to 3-4 years focused on research, depending on country and structure.

Practical implication:

  • If you already have a research master’s comparable to US core preparation, you may be ready for faster research onset.
  • If your prior training is less mathematical/technical, a strong master’s or pre-doc can be a high-return step (more on pre-docs later).

The economist job market (stylised calendar)

The market has variations, but the core rhythm is consistent and centralised:

  • Summer (before the market): finalize a strong draft of your job market paper (JMP).
  • September-November: submit applications; letter writers upload letters (these are essentially your job references).
  • December-January: first-round interviews, often around the ASSA/AEA annual meeting period (see: AEA - Understanding the Job Market).
  • January-February: flyouts (those interested in hiring you will fly you out to participate in full seminars + meetings).
  • February-March: offers.
  • July-September: start date.

Job market tools commonly used:

Tenure and ‘publish or perish’ (universities)

If you manage to navigate the highly competitive job market and land a job as assistant professor, now you have the ‘small’ task of getting tenure. For research faculty, the incentive system is usually a version of:

  • You have a limited window (often 6-8 years) to build a research record.
  • Publication quality and peer evaluation matter.
  • Seminars and conferences are part of how research is validated.

Research vs policy production (policy institutions)

Policy institutions also value rigour, but the production function differs:

  • Publications may be welcome, but policy relevance and internal deliverables matter.
  • Some research is published as working papers, technical notes, or institutional reports.
  • Clearance/approval processes may exist, depending on the employer.

Postdocs (when they matter in economics)

Postdocs are less structurally necessary in economics than in some lab sciences, but they exist:

  • Elite postdocs: often help to ‘buy time’ to strengthen a publication pipeline before tenure pressure.
  • Market-clearing postdocs: can be a bridge when a preferred placement did not materialise immediately.

Should you do a PhD (vs a master/pre-doc)?

In a separate blog, I wrote about the different types of economists.

A PhD is usually worth it when:

  • You want to be academic research faculty or a research economist in a policy institution
  • You enjoy research work for its own sake.
  • You are comfortable with long horizons and delayed gratification.

A PhD may not be necessary when:

  • You want to be a policy economist at a policy institution, in a policy track that values applied expertise over publications.
  • You want to work in industry or consulting, where many roles are accessible with a strong masters + skills.

Key takeaways

  • A PhD is training for independent research, not just advanced coursework.
  • The ‘job market paper’ is a core career asset, not a side project.
  • Your best next step depends on which rewards you want: publications, policy impact, or industry outcomes.

Checklist

  • Can you describe the job you want using the 2×2 map of what economists do?
  • Do you enjoy research tasks weekly (reading, coding, writing, presenting)?
  • Do you have the maths/statistics background for core PhD courses?
  • If not yet, is a pre-doc or research master’s the right bridge (later section)?

Getting into an economics PhD: Applications, signals and preparation

The application package (what is commonly required)

Most econ PhD applications weigh:

  • Transcripts (math, statistics, econometrics matter a lot).
  • GRE (quantitative score is a common filter in many places).
  • English proficiency (e.g. TOEFL/IELTS where required).
  • Letters of recommendation (extremely important; detail and credibility matter).
  • Statement of purpose (clear interests, realistic fit, evidence of preparation).
  • Sometimes: writing sample (varies by programme).

Useful AEA guidance pages:

A key clarification: economics is not a ‘lab model’ PhD

In many economics PhDs, you are admitted by a committee into a programme. You do not usually apply ‘to work in Professor X’s lab’ as the default structure. Faculty fit matters, but admission is typically programme-based.

Implication:

  • You should apply broadly to programmes where multiple faculty could plausibly advise your interests.
  • Your goal is not one ‘perfect professor’. It is a training environment with depth in your areas.

What signals matter most in practice

Common strong signals:

  • Maths preparation: beyond basic calculus. Linear algebra and probability are often essential.
  • Econometrics and statistics: proof-based or rigorous sequences help.
  • Research exposure: thesis, RA work, pre-doc, research assistantships.
  • Coding: ability to work independently with data (Python/R/Stata; version control is a plus).
  • Letters: from people who have seen you do research-like work.

Common mistakes to avoid

  • Applying ‘because I like economics’ without evidence of technical readiness.
  • Underinvesting in letters (generic letters are damaging).
  • Treating English as an afterthought. You need to read fast and write clearly.
  • Having interests that are too broad and not connected to any preparation.

For francophone / French-university backgrounds: How to bridge the gap

Many strong African francophone candidates face a predictable gap: the transition to English-first, proof-heavy, and empirics-heavy training environments.

Practical ways to close that gap:

  • English: aim for reading research papers weekly and writing short research memos.
  • Maths (if you do not have a scientific baccalaureate and or math undergrad): strengthen linear algebra, probability, and (when possible) real analysis.
  • Proof-based micro: if your curriculum was more descriptive, add rigour before PhD cores.
  • Empirical toolkit: become fluent in one workflow (data cleaning - analysis - tables/figures - write-up).
  • RA culture: learn how to work with a Principal Investigator (PI): clean deliverables, reproducible code, proactive updates.

Key takeaways

  • PhD admissions are signal-driven.
  • The best signals are: (i) technical readiness, (ii) research exposure, and (iii) strong letters.

If your current curriculum did not emphasise these signals, build them deliberately.

Checklist 

  • Math: linear algebra + probability done?
  • Metrics: at least one rigorous econometrics sequence done?
  • Coding: can you replicate a paper’s main result from a public dataset?
  • Research: at least one serious project with feedback?
  • Letters: two to three letter writers who can speak to your research potential?
  • English: can you read papers and present them without translating line-by-line? 

Pre-docs, masters and mentorship: Practical pathways to an economics PhD

Pre-docs / RA-ships: What they are and why they exist

A ‘pre-doc’ is usually a one or two year full-time research assistant role. It is designed to:

  • Build hands-on research skills.
  • Help you test whether research is a good fit.
  • Generate strong letters from active researchers.
  • Strengthen your technical portfolio (coding, data, empirical design).

Where to find structured information and postings:

Master’s programmes as a signal and as training

In many cases, a strong masters is the cleanest bridge between an undergraduate curriculum and PhD-level expectations.

A research-oriented masters can:

  • Provide rigorous micro/macro/metrics sequences.
  • Produce a transcript that is legible to PhD committees.
  • Create access to letters and research assistant positions.

Examples (non-exhaustive):

Africa-focused capacity-building and pathways:

Mentorship programmes: high leverage, especially for underrepresented backgrounds

Mentorship can reduce information frictions that disproportionately hurt students outside the main networks.

Programs worth knowing:

A practical ‘resource map’ of where to search

Academic job market tools

Pre-doc/RA searches

Policy institution entry programmes

Central bank research careers

Key takeaways

  • If a PhD is your target, the highest-return bridges are often: (i) a research-oriented master’s, and/or (ii) a strong pre-doc.
  • Mentorship is not 'optional'. It is a serious input into information and strategy.

Checklist

  • Can you name ten pre-docs and five master’s programmes that fit your profile?
  • Can you explain why each is a fit (skills gap, letters, field exposure)?
  • Are you building a portfolio (code, research memo, replication, thesis)?
  • Have you joined at least one mentorship network if eligible?

What to study in undergrad (if you want to keep the economics PhD option open)

It would be easy to assume that simply studying economics at undergrad is enough to keep the PhD option open. In reality, wherever you are studying economics, the choices you make as early as your first year of undergrad, be they classes (US) or optional modules (UK/Europe), play an important role. If you only start thinking about a PhD in your last year of undergrad, you are already at a disadvantage.

The core toolkit: minimum viable preparation

If you want to preserve the PhD option, aim to graduate with:

  • Maths: multivariable calculus, linear algebra. Add real analysis if possible.
  • Probability and statistics: probability theory + mathematical statistics.
  • Econometrics: at least one rigorous sequence, ideally with proofs and applications.
  • Programming: Python or R (plus Stata is common in applied economics).
  • Writing: the ability to write clearly and concisely is a differentiator.

Three sample pathways for the US context (templates you can adapt)

Path A: Economics + Mathematics (classic research track)

  • Year 1-2: calculus, linear algebra, intro stats.
  • Year 2-3: probability, mathematical statistics, intermediate micro/macro.
  • Year 3-4: econometrics, real analysis (if available), research thesis.

Path B: Engineering / CS + Economics (empirical/industry-friendly)

  • Strong math and programming backbone.
  • Add econometrics, causal inference, and at least one applied field.

Path C: Business / Public policy + Statistics (policy/industry bridge)

  • Build a serious empirical toolkit.
  • Add micro theory and econometrics rigour to avoid ‘surface economics’.

Habits that matter more than one extra class

  • Read one paper per week (even if you understand 60% at first).
  • Write short summaries. Practice turning intuition into clean statements.
  • Keep your code reproducible. Make outputs easy to verify.
  • Seek feedback early. Iteration is the job.

Key takeaways

  • For economics PhD readiness, math + econometrics + coding are the backbone.
  • Language is also a technical skill: English reading and writing compound over time.

Checklist 

  • Do you have linear algebra and probability?
  • Have you taken a serious econometrics course (not only descriptive stats)?
  • Can you code end-to-end (clean -> estimate -> table/figure -> write-up)?
  • Do you have a research artifact you can show (thesis, replication, RA output)?

Links to useful resources 

(i) Job market / academic hiring

(ii) Pre-docs / RA portals

(iii) Mentorship programms

(iv) International organizations and policy institutions careers

(v) Rankings (use as inputs, not as truth)

(vi) Selected master’s programmes (examples, non-exhaustive)

PhD

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