01-introduction.md

Illustrative teaching example. The paper, setting, policy, and every number below are fictional and exist only to demonstrate American Economic Journal: Applied Economics (AEJ: Applied) house style. No real-paper facts and no real policy are stated. Corresponding skills: aeja-writing-style, aeja-topic-selection, aeja-literature-positioning, and aeja-identification.

Worked Example: An AEJ: Applied Introduction (before → after)

This demonstrates the AEJ: Applied introduction arc from aeja-writing-style: question → why credible identification is hard → the design that solves it → headline causal estimate (with a standard error) → mechanism → policy/economic lesson → brief roadmap.

Two AEJ: Applied house rules drive the rewrite and distinguish it from a field-internal note or an AER agenda paper:

• AEJ: Applied is the AEA's empirical applied-microeconomics journal — the contribution is a credibly identified answer to a substantive question of broad applied-micro interest, not a policy verdict (AEJ: Economic Policy) and not a general-interest agenda swing (AER) (aeja-topic-selection).
• The identification is the star: name the design plainly and put the headline estimate, with units and a standard error, in the first breath (aeja-writing-style, aeja-identification). AEA permits significance stars but expects standard errors — so the magnitude and its SE live in the sentences.

Illustrative paper (fictional): "Free Bus Passes and Teen Employment: Evidence from a Staggered Municipal Rollout." Fictional setting: a transit authority gives free bus passes to high-schoolers, phased across invented municipalities over several years; the paper estimates the effect on teen employment using a heterogeneity-robust difference-in-differences design on invented administrative data. Every magnitude is invented.

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Before (buries the question; leans on TWFE; reports only stars)

We study public transit and youth labor markets, a topic of longstanding interest. Using a panel of municipalities, we estimate a two-way fixed-effects regression of teen employment on an indicator for whether a free-bus-pass program is in place, controlling for municipality and year fixed effects and a vector of covariates. The literature on transportation and employment is extensive. We find that the program has a positive and statistically significant effect on teen employment (significant at the 1% level). Section 2 reviews the literature, Section 3 describes the data, Section 4 presents the specification, Section 5 the results, and Section 6 concludes.

What is wrong (against aeja-writing-style / aeja-topic-selection / aeja-identification):

• No economic question and no magnitude on page one. A broad applied-micro reader cannot tell what is being measured or how big it is — only that something is "significant."
• Leads with the estimator and a wall of controls instead of the design that makes the estimate credible. "Two-way fixed effects" on a staggered rollout is exactly the setup AEJ: Applied referees flag for negative-weighting bias (aeja-identification Path B).
• Stars without standard errors or a magnitude. "Significant at the 1% level" carries no point estimate, no units, no SE — AEA expects the SE and the number.
• Throat-clearing ("longstanding interest," "the literature is extensive") with vague stakes and no contribution sentence.
• Over-signposted roadmap doing the work the argument should do.

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After (AEJ: Applied arc — question + credible design + estimate-with-uncertainty, contribution early)

Does cheap transportation help teenagers find work? Using a staggered municipal rollout of free high- school bus passes, we find that the program raised teen employment by 3.1 percentage points (s.e. 0.9) — a 7% increase off a baseline employment rate of 44% — concentrated among teens living far from the job centers their town's transit network connects to. *(question + headline causal estimate + its uncertainty

• who it moves, in the first breath.)*

Estimating this is hard because towns that adopt transit subsidies differ from those that do not, and a naive comparison conflates the program with the local economies that chose it. (why credible identification is hard — selection into adoption.) Because the passes were phased in across municipalities over several years, we use a difference-in-differences design with staggered timing, estimated with a heterogeneity-robust estimator (Callaway–Sant'Anna) rather than two-way fixed effects, which would mix in contaminating comparisons between newly treated and already-treated towns. We show flat pre-trend leads, report a Goodman-Bacon decomposition, and bound the estimate under a plausible violation of parallel trends. (the design that solves it, named plainly; aeja-identification Path B.)

The 3.1-point effect (s.e. 0.9) is stable across alternative samples, clustering at the municipality level with a wild-cluster bootstrap given the modest number of treated towns, and an honest-DID bound keeps the effect positive under pre-trend violations up to the largest observed pre-period deviation. (robustness and inference matched to the design; aeja-robustness.) The employment gain is largest for teens in transit deserts and near zero where job centers were already walkable, pointing to reduced commuting cost, not income effects, as the mechanism. (mechanism that distinguishes channels.)

Our contribution is a credibly identified magnitude for a policy lever many cities can pull: we show that a low-cost transit subsidy moves teen employment, and that the effect operates through access rather than income. This is the local average effect for marginal teens in transit-poor neighborhoods; we do not claim it transfers to dense cities with universal transit, and the estimate is an intent-to-treat effect of program availability. (contribution as question + magnitude + lesson, with a calibrated scope; aeja-literature-positioning.) Beyond this setting, the result suggests that commuting frictions are a binding constraint on youth employment, relevant to the design of school-to-work and transit policy. (broad applied-micro lesson.)

The paper proceeds as follows. Section 2 describes the rollout and data; Section 3 lays out the design and pre-trend evidence; Section 4 reports the main estimate, robustness, and heterogeneity; Section 5 examines the access mechanism. (brief roadmap — no over-signposting.)

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Why the "after" meets the AEJ: Applied bar

Mapped to this pack's skill checklists:

• First paragraph states the question + the causal magnitude + its uncertainty — the 3.1pp effect (s.e. 0.9) appears immediately, with units and a standard error, never a bare asterisk (aeja-writing-style: "Intro ¶1 gives the economic question and the headline number with uncertainty").
• The identification is named and defended before the machinery — staggered DID, heterogeneity-robust estimator, flat leads, Bacon decomposition, honest-DID bound — matching aeja-identification Path B and pre-empting the modal AEJ: Applied referee objection.
• Inference matches the design — clustering at the municipality level with a wild-cluster bootstrap for few clusters (aeja-robustness).
• Contribution is an answer with calibrated scope, not a method — "a credibly identified magnitude for a policy lever," plus an explicit statement that it is a local ITT effect that need not transfer, satisfies aeja-literature-positioning and aeja-topic-selection.
• Mechanism distinguishes channels — access vs. income — which is what turns a coefficient into an economic finding.
• Sibling check passes: this is method/identification-driven applied micro (AEJ: Applied), not a general-interest agenda paper (AER) and not a program-ROI policy verdict (AEJ: Economic Policy).

Next: see ../exemplars/library.md for real, web-verified AEJ: Applied papers whose introductions execute this arc, ../../skills/aeja-replication-package/SKILL.md for building the analysis so it clears the AEA Data Editor reproducibility check, and ../code/ for a runnable empirical command chain. For the venue-neutral referee objections a staggered-DID claim must pre-empt, see ../../../shared-resources/empirical-methods/reviewer-objection-checklist.md.