Codebook

This document defines every column in every data file. Read this before doing analysis.

data/moratorium_inventory.csv

One row per moratorium instrument. 222 rows total.

enacted_status values

This is the column most users want for filtering. The values:

• active — moratorium is currently in force (~137 rows)
• extended — moratorium was extended past its original sunset and is currently in force (~11 rows)
• replaced — moratorium has expired and a permanent ordinance has been adopted in its place (~27 rows)
• expired — moratorium has lapsed without a documented replacement (~18 rows)
• rescinded — moratorium was affirmatively repealed before its expiration date (~5 rows)
• pending — moratorium has been proposed but is not yet in force (~24 rows; e.g., a public hearing is scheduled but the vote hasn't happened)

To filter to "moratoria currently in force":

in_force = df[df["enacted_status"].isin(["active", "extended"])]

To filter to enacted-only (current or past):

enacted = df[~df["enacted_status"].eq("pending")]

Notes on the data

• Multi-sector instruments (one ordinance covering data centers + solar + battery storage + wind) are recorded as one row. The sectors column gives a clean multi-label list; the trigger, legal_basis, and outcome text carry the prose.
• Extensions of the same instrument (e.g., Resolution X adopts a 6-month moratorium, Resolution X-A extends it 6 more months) are recorded as updates to the original row's current_status and enacted_status, not as new rows.
• Successive new instruments are recorded as separate rows. If a county adopts a moratorium, lets it lift or expire, and then later adopts a new moratorium, each is a separate row. Oliver County, ND has three such phases — three separate moratoria, three rows, distinguished by (Phase 1) / (Phase 2) / (Phase 3) in the jurisdiction field and by the -p1 / -p2 / -p3 suffix on moratorium_id.
• Pending moratoria (proposed but not yet adopted) are intentionally included with enacted_status = pending because the public proposal is itself politically meaningful. Filter on enacted_status if you want enacted-only counts.
• [VERIFY] flags mark fields where we couldn't fully confirm the value from primary sources. Most often this is because the local government hasn't posted minutes or signed-ordinance text online yet.

data/state_legislation.csv

One row per state bill. 413 rows total.

This file tracks state-level legislation in 2025–2026 that relates to moratoria — bills authorizing local moratoria, prohibiting them, preempting them, or imposing state-level moratoria of their own. Most are still pending; a small number have been enacted.

data/summary_stats.json

A single JSON object with top-level aggregates. Useful for embedding live counts on a page or programmatic comparison across releases.

Top-level keys:

• total_local_moratoria: 222 — total rows in the inventory
• total_state_bills: 413 — total rows in state_legislation.csv
• states_with_moratoria: 30
• states_without_moratoria: 20
• moratoria_with_verify_tags: 62 — rows with at least one [VERIFY] flag remaining
• moratoria_without_verify_tags: 160
• enacted_status_breakdown: {active, extended, replaced, expired, rescinded, pending} — the breakdown of the 222 rows by enacted_status
• top_states_by_moratoria: list of [state_name, count] pairs, top 15
• top_states_by_bills: list of [state_name, bill_count] pairs, top 15
• states_by_activity_level: count of states in each closed-vocab activity bucket
• state_details: per-state object with abbreviation, activity_level, local_moratoria_count, state_bills_count, file, and (for special cases like NJ) an activity_notes field that preserves qualifying context

data/structured_extractions.jsonl

One JSON object per line. The file contains 864 lines:

• 526 lines are successful extractions (records with extraction != null and error == null)
• 338 lines are LLM-call errors retained for transparency (records with error != null)

The n=348 cohort referenced in the working paper is the subset of successful extractions with confidence >= 0.4. Use this filter when computing clause-prevalence percentages:

import json
records = [
    json.loads(line) for line in open("data/structured_extractions.jsonl")
    if json.loads(line).get("error") is None
    and json.loads(line)["extraction"]["confidence"] >= 0.4
]
assert len(records) == 348

Each record has fields:

• source_file: relative path to the original document (in research/_originals/ of the private working repository)
• url: original URL the document was downloaded from (where known)
• extraction: the structured object — jurisdiction, state, instrument form, sectors, durations, findings, definitions, common clauses, sector-specific clauses, confidence, narrative summary
• error: null on success; an error message string on failure

The full schema (60+ fields organized into five tiers) is in data/moratorium-extraction.json (JSON Schema format). Records where error is not null should be excluded from analysis.

data/clause_extraction_analysis.json

The pre-computed aggregate analysis from structured_extractions.jsonl filtered at confidence >= 0.4. Used to generate the percentage tables in tables/.

Fields:

• total_records: 348
• min_confidence_filter: 0.4
• confidence: {min, max, mean, median, brackets}
• geographic: state counts, jurisdiction-type counts
• temporal: year breakdown of adoption dates
• legal: instrument forms, authority types, sector counts
• clause_prevalence: detailed breakdowns of every clause type in the 44-clause taxonomy (universal, common, sector-specific, definitional approaches, findings detail levels)

A note on dates

Where possible, dates are ISO-8601 (YYYY-MM-DD). When a day is unknown but month+year are known, we use YYYY-MM. When only the year is known, YYYY. When the date is uncertain (sourced from news rather than minutes), we may write something like 2025-12-17 reported or January 2026 [VERIFY]. Spreadsheet tools may need help parsing these; the examples/ folder shows how.