# Phase 0 — Feasibility report (WP37)

Date: 2026-11-01 (UTC). Author of record: S.-P. Boucher (UQO), pipeline executed by Claude Code.

# 1. Working directory and repository

• Project created at Recherche-UQO/economie/hidden-architecture-grocery-prices/ from the UQO working-paper template (_modele-working-paper). Nothing pre-existing was overwritten; WP numbers 1–35 are taken, WP36 is an unnumbered stub (economie/used-car-market-liquidity), so WP37 is free.
• Git initialised locally (main). Remote gitsrv:srv/git/wp37_uqo.git not yet created (creation on spbgit requires the PI's go-ahead; nothing is published externally).
• Mandate stored verbatim in CLAUDE.md.

# 2. Compute, toolchain

# 3. Data access — what actually exists

Food-Ka is owned by the PI; the production SQLite database (M2U64:~/apps/food-ka/data/foodka.db, WAL mode) was copied with a consistent .backup from a read-only connection (production untouched). Checksum verified. Provenance in data/raw/README.md.

Public cross-check (2026-11-01 ~08:00 UTC): homepage shows 50 760 products, 54 banners, 8 631 on promotion — identical to the snapshot's active=1 rows. The mandate's figures (50 886 / 54 / 8 590) were the homepage values a few hours earlier; they are source-feed listings, not unique products. robots.txt disallows /api/ to crawlers; the API was queried once for this cross-check only and is not a data source.

# 3.1 Schema (observed)

products (124 579 rows: 50 760 active, 73 819 deactivated) — uid = source:external_id, name, brand, category (18 canonical), category_raw, size_label, price, regular_price, price_label, on_sale, unit_price, unit_price_label, in_stock, details (JSON), first_seen, last_seen, updated_at, miss_count, active. price_log (300 613 rows: uid, ts, price) — event log: one row at first observation and one row at every change of price (NULL = price disappeared from display). No row when the price is unchanged. regular_price, on_sale, in_stock are not historised. sync_log (15 630 rows) — one row per connector pass (57 sources × ~255–308 passes, median gap 7.2 h, 2026-08-12 → 2026-11-01), with found/added/updated/removed counts. product_links (2 537 uids in 1 103 cross-banner groups) — Food-Ka's own brand+size+name matching (not GTIN based). off_cache (25 630) — Open Food Facts barcodes attached by a conservative brand/name/size lookup (derived, not source-provided).

# 3.2 Data-generating process (from the connector code, read-only copy in data/raw/foodka-src/)

• 57 active source feeds = 54 with active products, belonging to far fewer retail companies (Loblaw, Metro Inc., Sobeys/Empire, Walmart, Costco, independents, four pharmacy banners, the SAQ monopoly); several banners appear twice (online storefront feed + Flipp circular feed). A retailers table (company → banner → feed → channel) is built in Phase 1 and ownership is verified against public corporate information in Phase 2.
• Two channels: (a) online storefront feeds (Shopify/WooCommerce JSON, Loblaw/Metro/Walmart/Costco/IGA sites scraped through residential proxy pools without store selection); (b) weekly circulars via Flipp for postal code H2X 1Y4 (Montréal) with valid_from/valid_to, original_price, dollars_off, fine print.
• Geography: no store identifier anywhere. Circulars are regional (Montréal zone); online prices are whatever store the retailer's site assigned to the proxy session. → Store-level geography is unavailable (H4 and travel-cost components of Model E cannot be estimated from Food-Ka).
• Identifiers: Metro and Super C external_id are valid GS1 UPC-A codes (100 % check-digit valid, n = 1 954 active). Shopify/Walmart/Loblaw ids are internal SKUs (check-digit validity ≈ 10 %, i.e. chance). Exact GTIN matching is therefore only possible within Metro Inc.; everything else requires validated probabilistic matching.

# 3.3 Critical measurement finding — store-mixture noise in scraped banners

For anti-bot banners (club_entrepot, metro, maxi, superc, provigo, walmart) the event log shows high-frequency oscillation among a small set of price values (e.g. Metro mayonnaise 890 mL alternating 5.77 / 6.99 / 7.49 / 7.79 / 7.99 within the same day; 54–68 % of change events revert to the previous-but-one value; median 4 distinct values per product; week-to-week Jaccard of value sets ≈ 0.5). This is consistent with each scrape being served by a different (unknown) store, not with genuine intra-day repricing. Consequences, to be handled in Phase 1 and preregistered in Phase 3:

• a banner-level "posted price" at time t must be defined as a statistic of the daily/weekly value set (mode, max = likely shelf regular, min = likely best store/promo), with sensitivity analysis;
• intra-week volatility for these banners is not interpretable as price flexibility; H2 must be tested on weekly aggregates and separately on circular feeds (clean weekly cycles);
• the amplitude of the value set is itself a (store-unidentified) lower bound on within-banner cross-store dispersion — a usable, honestly-labelled quantity. Shopify/WooCommerce feeds (single online store) show far fewer events and no such cycling.

# 3.4 Variable availability (summary; full inventory in Phase 1)

# 4. External benchmarks

Statistics Canada WDS reachable: table 18-10-0004-01 (CPI monthly NSA, Quebec = member 11, Montréal = 13; "Food purchased from stores" = member 4; latest 2026-09) and 18-10-0245-01 (monthly average retail prices for 110 selected products, Quebec; latest 2026-08). Overlap with the Food-Ka window is only Aug–Sep 2026 (2 months) — validation of QGPAI against official indices will necessarily be weak and labelled as such.

# 5. API credentials (minimal tests, 2026-11-01T08:02Z)

Planned LLM use is limited and auxiliary: (i) second annotator for the stratified match-validation sample (human = PI adjudication remains the reference), (ii) nothing in estimation. Every call is logged with model id, tokens and cost.

# 6. GO / NO-GO gates

# 7. Expected compute

Whole pipeline (panel reconstruction, matching, fixed-effects models, bootstraps, MIP baskets, rolling-origin forecasts) is expected to run in well under one hour on the laptop; no cluster job required.