| name | jpam-transparency-and-data |
| description | Use when preparing the replication / transparency materials for a Journal of Policy Analysis and Management (JPAM) manuscript — depositing data and code in a public repository so the reported results can be reproduced, with an honest exemption path for restricted administrative data. Prepares the package; it does not waive requirements. |
Transparency & Replication Data (jpam-transparency-and-data)
JPAM publishes program evaluations that often inform real spending decisions, so the evidence must be
reproducible. The journal expects authors to archive the data and code behind the reported
results in a suitable public repository, with a clear data-availability statement. Build the package as
you go so it does not stall acceptance — and plan early for the restricted administrative data
common in policy work. Verify the current wording in Wiley Authors / Research Exchange before upload
(检索于 2026-06-20；以官网为准).
When to trigger
- Building the replication package (data + code + documentation)
- A manuscript is heading toward acceptance and materials are requested
- Data are restricted (administrative, IRB-protected, provider-licensed) and you need the exemption path
- Writing the data-availability statement
What to prepare
- Public deposit. Place the replication materials in a recognized repository (e.g., the project's
ICPSR/openICPSR archive, Harvard Dataverse, OSF, or a journal-designated repository) with a
persistent identifier — not a personal website or transient cloud link. Recent JPAM data-availability
statements often use a JPAM/Harvard Dataverse repository, while Research Exchange controls the exact
repository prompt.
- Reproduce every reported number. A master script regenerates every table and figure from the
raw/constructed data. Exhibit numbers in the manuscript match the package output exactly.
- Documentation. A README covering data provenance, construction steps, software/package versions,
seeds for stochastic steps, and the exact command to reproduce each exhibit.
- Data-availability statement. State what is shared, where, and under what license; if data are
restricted, state precisely why and how a replicator can obtain access.
Restricted administrative data (the policy-research case)
Policy evaluation often runs on linked administrative or survey microdata that cannot be posted.
JPAM's transparency expectation is met honestly by:
- Explaining the restriction (legal, IRB, data-provider license) in the data-availability statement.
- Providing access instructions — the application process, provider contact, and approximate timeline
so an independent replicator could obtain the same data.
- Posting all code plus any shareable derived/aggregated files and, where feasible, synthetic or
simulated data that let the code run end-to-end.
Build-as-you-go checklist
Anti-patterns
- Treating the package as a post-acceptance afterthought (it can gate publication)
- Depositing code that does not actually reproduce the printed tables/figures
- A personal URL or expiring cloud link instead of a persistent repository
- Claiming data are restricted with no access path or synthetic substitute
- Undocumented, un-seeded, unpinned code that only "works on my machine"
Calibration anchors (hedged)
- Policy evaluations lean heavily on restricted administrative data; an honest restriction note plus
a real access path and runnable code on synthetic/derived data is the expected, accepted route — not a
loophole to skip transparency.
- The exact deposit requirement, repository prompt, and reproduction-check workflow can change; confirm
the current data-policy wording in Wiley Authors / Research Exchange.
- Build the package alongside the analysis: retrofitting reproducibility after acceptance is where
policy papers stall.
Worked micro-example (illustrative)
A welfare-reform evaluation uses linked state UI and TANF records that cannot be posted. The package
still meets the bar: a data-availability statement explains the licensing restriction and names the
state agency's data-request process and timeline; all code is deposited with a master script; a
synthetic dataset matching the variable structure lets a replicator run the full pipeline; and
shareable aggregated tables are included. An independent researcher could obtain the real data and
reproduce every number. (Illustrative.)
Output format
【Repository】public archive + persistent ID — chosen? [Y/N]
【Reproduces results?】master script verified locally? [Y/N]
【Documentation】README + provenance + seeds + pinned versions? [Y/N]
【Restricted data?】restriction explained + access path + synthetic data?
【Data-availability statement】drafted? [Y/N]
【Next】jpam-review-process
Supplementary resources
