Skills Data Science QE Manuscript Writing Style Guide

QE Manuscript Writing Style Guide

v20260724
qe-writing-style
This guide helps authors polish the prose, abstract, and introduction of quantitative economics manuscripts for a general-interest Econometric Society readership. It emphasizes structuring the paper to clearly present both a substantive economic question and a quantitative method. It adheres to QE house rules, requiring findings to be communicated through point estimates with standard errors and confidence sets, rather than relying on statistical asterisks.
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Overview

Writing Style (qe-writing-style)

When to trigger

  • The prose buries the economic question under method or notation
  • The abstract describes the topic but never states what was found or built
  • The introduction reaches the model/estimator before the reader sees the question
  • Statistical claims lean on asterisks or vague intensifiers instead of magnitudes and uncertainty

QE house style: a quantitative method serving a substantive question

QE is read across all of economics through an Econometric Society lens, so a paper must make both the substantive economic question and the quantitative apparatus (structural model, estimator, experiment, or simulation) legible to a smart non-specialist early. Format facts that shape the writing: the abstract is ≤150 words; the title page carries keywords and affiliations; the manuscript is 1.5/double-spaced, ≥12pt, ≤32 lines per page, with figures and tables in-text. Crucially, QE's house rules forbid asterisks and boldface for statistical significance — the prose and exhibits must communicate findings through point estimates with standard errors and confidence/coverage sets, so write magnitudes and uncertainty into the sentences themselves. QE also applies its own reference style at copyediting, so keep citations consistent rather than chasing a format.

The introduction arc (QE template)

  1. The question — one or two sentences, plain language, stakes clear.
  2. Why it is hard quantitatively — the measurement, identification, or computational obstacle.
  3. The approach — the data + model/estimator/experiment that resolves it, in one paragraph.
  4. The headline result — the key estimate or quantity, with units and a standard error / coverage set, stated early.
  5. Mechanism & interpretation — what it means; the economic frame in a sentence.
  6. Contribution & lesson — placement in the literature + what transfers beyond this setting.
  7. Roadmap — brief.

Abstract: state the finding (≤150 words)

  • Open with the question and the approach in one breath, then state the result with a number and its uncertainty.
  • For structural/computational work, name the quantity (an elasticity, a welfare number, a counterfactual) and the model that delivers it.
  • Close with the broad lesson. No throat-clearing; stay within 150 words.

Sentence-level craft

  • Active voice; short declaratives for the key claims.
  • Define notation once; do not make the reader hold five symbols to parse a sentence.
  • Quantify with uncertainty ("a 7.3% rise, s.e. 1.1") rather than "significantly affects" or asterisks.
  • Calibrated confidence reads as competence; hedge only where the evidence requires it.
  • Relocate heavy derivations and extra results to the Supplemental Appendix (≤25 pages); the main paper stays self-contained.

Checklist

  • Abstract states the actual finding with a number and its uncertainty, ≤150 words
  • The question is on page one in plain language
  • The headline estimate appears early in the intro, with units and a standard error / coverage set
  • The broad lesson ("beyond this setting") is explicit
  • No significance asterisks or boldface in prose or exhibits
  • Magnitudes are quantified, not vaguely intensified
  • Notation introduced once; references consistent (QE styles at copyediting)

Anti-patterns

  • An abstract that names the topic but never the result, or runs past 150 words
  • Leading the intro with the estimator ("We use indirect inference...") instead of the question
  • Reporting significance with asterisks/boldface (QE forbids this)
  • Vague magnitude language ("significantly", "substantially") with no number or uncertainty
  • Notation overload in the introduction

Worked vignette: an abstract rewrite (illustrative)

A draft abstract reads: "We study how minimum wages affect employment using a structural model of labor demand, and discuss policy implications." It names a topic, never a finding. The QE rewrite states the quantity with its uncertainty: "Estimating labor demand on firm-level data, a $1 minimum-wage rise lowers low-wage employment 1.2% (s.e. 0.4) but raises retained workers' earnings 3.1% (s.e. 0.7); net surplus rises in low-turnover sectors and falls in high-turnover ones." (Illustrative, ≤150 words.) The reader leaves with a number, its uncertainty, and the lesson.

Referee pushback and the prose fix

  • "The abstract never states what you found." → Lead the second sentence with the headline quantity and its standard error.
  • "The intro reaches the estimator before the question." → Re-order to the QE arc: question, hardness, approach, result early.
  • "Magnitudes are vague." → Replace every intensifier with a number and a coverage set.

Output format

【Abstract verdict】states finding + number + uncertainty, ≤150 words? [Y/N] — fix: ...
【Intro arc】question / hardness / approach / result / interpretation / contribution present? [Y/N each]
【Headline estimate in intro】present + units + SE/coverage? [Y/N]
【Significance reporting】asterisk-free, SEs/coverage shown? [Y/N]
【Broad lesson stated】[Y/N]
【Next step】qe-replication-and-data-policy or qe-review-process
Info
Category Data Science
Name qe-writing-style
Version v20260724
Size 5.55KB
Updated At 2026-07-29
Language