Drilled down through three layers. Cache should be warm now — same document root (#NTAB4Q), same outline, and overlapping section reads on each turn.

Here’s what I pulled, in case you want to use it:

• Outline of the thesis (#NTAB4Q): four chapters, with theory in §2 (#9P9SE9) and Latvia empirics in §3 (#8QCVWV).
• §2.2 Information asymmetry, signaling, screening (#SNPQS9): 10 paragraphs — Akerlof → Spence → Laffont/Tirole → procurement analogue → Spence 2002 → screening → simultaneous signaling/screening (#LTCKYG).
• §2.3 Supplier’s participation decision (#MPRPUM): builds K(s), the three PTC failure modes (#ZDWUN7, #M3AWNT, #FYGFU5), the two-gate model, and the bid rule

  Bid if: P_screen × P_compete × (V − C) − K(s) > OC (#2CCJRE)
  plus three behavioural extensions in #9NNAKR (loss aversion, ambiguity aversion, threshold effects).

• §3.1 Latvian landscape (#DMYF2Y): PIL + Cabinet regs, 2026 reform timeline, IUB/TED data; KIBS = 6.8% of lot records / 7.7% of value across CPV 72/73/79 (#T2K94W).

Cache test from my side: each subsequent call only had to fetch the new block ids; outline and ancestry came back consistently across turns without re-reading.

Findings — bid/no-bid signals in the gold-standard sample (n=1,278 KIBS tenders)

1. The headline. 34% of these knowledge-services tenders attract only one bid. Mean bidder count 2.93. That single-bid rate is the central empirical hook for the thesis.

2. Time to prepare matters — and it’s the cleanest design lever in the data.

Pushing prep time past ~25 days is associated with ~14pp lower single-bid rate and >1 extra bidder on average. This fits the signaling/transaction-cost story directly: longer windows lower the marginal cost of producing the bid signal, drawing in suppliers whose expected utility was previously negative.

3. CPV split exposes the IT-services problem.

IT tenders are de facto sole-source half the time. Worth a dedicated subsection.

4. Buyer-history is sticky. Buyers with ≥50% historical solo-bid rate keep getting solo bids (40.9%) vs. those with no solo history (28.6%). Reputation effects on the buyer side — suppliers self-select away from “known to be wired” buyers. This is a screening-architecture-credibility result.

5. Procedure type. Open procedures: 31.7% solo. “Small” (below-threshold): 36.2%. Modest gap.

6. Award method (cautious — small cells). MEAT (n=10) draws 4.0 bidders / 10% solo; price-competitive (n=1,200) draws 2.94 / 34.8%. Suggests quality criteria attract participation, but coverage is too thin to claim it. Note also: price_weight is coded for only 74 tenders — that’s a coverage gap to flag in the methods chapter.

7. Buyer-type heterogeneity. Hospitals (5.88 bidders, 16% solo) and SOEs (3.29, 24%) attract more competition than municipalities (2.41, 38.9%) and “company” buyers (2.45, 46.4%). Capacity & repeat-game stories.

8. Repeat-bidder concentration. Top-15 firms (KPMG, PwC, EY, Alpha Baltic Media, PR Kvadrāts, Eiroeksperts, Interbaltija, Skrivanek…) generate 750+ of 3,699 bids. A handful of professional bidders dominate — consistent with the literature on procurement specialists who have amortised the bid-preparation cost.

Data-quality notes for the methods chapter:

• bid_status is coded for only 219/3,699 bids (~6%). Outcome analyses at bid level aren’t yet feasible at scale.
• bidder_origin ~6% coded; foreign participation can’t be measured yet.
• price_weight/quality_weight coverage 74/1,278 — the MEAT vs. price-only contrast is the main analysis blocked by this.
• 14 tenders have null bidder_count; one row in bidder_count literally contains the formula text — likely a sheet-template artefact to clean before publishing the figure.

Most thesis-ready chart: prep-time bucket × single-bid rate, segmented by CPV-72 vs CPV-79. That single 2×2 picture operationalises the signaling-cost mechanism on real Latvian data.

Poked around. Quick report:

Working

• list_documents, list_spreadsheets, get_document, read, query_spreadsheets, search_spreadsheets all return clean results.
• Document outline for #NTAB4Q renders correctly; read on section #MPRPUM returns its 18 child paragraphs with previews.
• Spreadsheets sidecar has the inventory + gold_standard_v12 (10 sheets) loaded.

Broken

• search_text errors out with "search failed: :missing_openai_api_key" — embedding search is dead. Worth fixing before you rely on it; semantic lookups across the draft and spreadsheets are currently unavailable.

Data nit (not a system bug, but surfaced while poking)

• In tender_level (sheet 2 of gold_standard_v12), row 3 looks like a coding-rule legend that got imported as a data row — single_bid = "1 if bidder_count=1; 0 otherwise", bidder_count = "Count of bids from bid_level. Formula: COUNTIF(...)", etc. It’s also why your single_bid GROUP BY shows a stray "1 if bidder_count=1; 0 otherwise" bucket alongside the real 0/1/null. Either drop that row at import or filter it out in queries (WHERE single_bid IN ('0','1')).
• Real single_bid distribution on the remaining 1,277 rows: 831 multi-bid, 432 single-bid, 14 null — i.e. ~34% single-bid rate among non-null lots, which is the headline number your participation story turns on.

Yes — everything’s wired up. Here’s a quick read.

Thesis (#NTAB4Q) is structured in four parts: theory (signaling/screening, supplier participation decision, KIBS amplification), the Latvian empirical chapter (data construction, quantitative patterns, qualitative validation), and a synthesis chapter on sub-market typology + 2026 reform assessment. The selected paragraph (#U57A7C) sits in the introduction and frames the research question precisely against the EUR 10k → 143k threshold reform.

Spreadsheets visible:

1. inventory.xlsx (ss_xl_46bd62d8086a_1, 17,098 rows) — document-level inventory of downloaded procurement files (procurement_id, doc type, file paths, formats, archive provenance). This is the raw document corpus index.

2. gold_standard_v12.xlsx — the analytical dataset, with 10 sheets:

   • bid_level (3,699 rows) — one row per bid; company, amount, status, scores, disqualification reasons, rank.
   • tender_level (1,278 rows, 40 cols) — the core unit of analysis. Has all the IVs the variable dictionary calls for: award_method_price_only, price_weight/quality_weight, time_to_prepare_days, ex_ante_deliverable_required, amendment_count, disqualification_rate, plus DVs bidder_count, single_bid, winner_was_cheapest, and history controls (buyer_hist_single_bid_rate, repeat_bidders).
   • company_id_map (1,038) — canonical company ledger with name variants and total bids.
   • population (913) — the IUB-derived sampling frame with KIBS classification, doc-availability flags, and gold_standard membership.
   • IUB_metadata (80) — looks like a smaller IUB extract subset.
   • special_cases_guide, VARIABLE_dictionary_v2, variable_dictionary (41 vars with gate/category/coding_rule/source), Primary_sources, normalization_rules — the codebook.

Querying works. Quick sanity check on the headline stat: across 1,278 tender-level rows, 433 single-bid tenders (33.9%) and mean bidder count 2.93 — consistent with the participation-deficit framing in the introduction.

Variable dictionary is well-structured (gate1/2/3 + Controls + DV) and maps cleanly to the signaling-cost model: Gate2 = evaluation uncertainty (price/quality weights, score publication, winner-was-cheapest), Gate3 = preparation/integrity costs (time, ex-ante deliverable, amendments, disqualification rate). That’s a clean operationalization to write up in §3.2/3.3.

Tell Jāzeps the data is queryable end-to-end — bid_level joins to tender_level on procurement_id/lot_id, company_id_map gives him repeat-bidder analysis, and population vs. tender_level lets him check selection bias from the gold-standard subsample.

OK — I’ve done a research run. Here are the notes.

Verification of headline tables in §2.3 (#7NBHCY)

The headline narrative survives. The “4+” column in Table 2.6 is the only thing I’d recheck before submission.

Data quality flags — these affect the regression in #3NHBCQ

The Gate 2 / Gate 3 IV slate the variable dictionary calls for is mostly empty in the current extraction:

Implications:

1. Proposition 1 — “marginal effect of PTC stronger for lower-value contracts” — is currently not testable. With bid_ceiling_value at 2.5% and award_value at 6.9%, there is no usable contract-value variable for tier interactions. This is the single most urgent extraction gap.

2. award_method_price_only looks miscoded. The gold-standard tender_level shows 1,205 price-only vs 57 MEAT (95/5). But the IUB population sheet (ss_xl_755d8c3b605e_4, n=913) reports 443 price-only vs 454 MEAT (≈50/50), matching §2.1’s claim of “22.9% MEAT, 43.6% price-only” at lot level (#FHLVHX). Most likely the extraction defaults missing/unparseable evaluation criteria to price-only. This invalidates the planned MEAT-vs-price-only stratification in the regression spec (#AN2Q5R).

3. ex_ante_deliverable_required has 5 positive cases of 87 coded — too rare to use as a Gate 2 indicator at present.

4. bid_level (3,699 rows) is largely unfinished: 3,480 have null bid_status; only 219 have bidder_origin, of which 209 are LV. Proposition 3’s international-supplier sensitivity to informal PTC has no quantitative purchase here — it will need to come entirely from the qualitative phase, and the introduction of the supplier-origin interaction term in #W66GFF should be softened accordingly.

What is currently runnable, and what it shows

(a) Time-to-prepare → participation, clean monotone effect. This is the strongest IV in the present dataset.

The gap between the modal 15–21 day band and the 30+ day band is ~9 percentage points of single-bid rate — a sizeable Gate 2 (signaling-cost) effect. This deserves a paragraph in #ET42TV or its own subsection; it’s a more direct test of the Gate 2 logic than the procedure-type contrast.

(b) Buyer-history equilibrium (supports §1.4 and #VT68CG).

The top vs bottom band gap is ~16pp — larger than the value reported in #VT68CG (12.2pp). The paper’s framing (“modest in magnitude… one signal among several”) is defensible but actually understates the effect.

(c) Repeat-bidder concentration (KIBS market structure, supports §2.5 thin-market argument).

• 598 firms (57.6% of the 1,039 unique bidders) submit only one bid across 2024–25
• The top 12 firms generated 675 bids — ~18% of all KIBS bids
• Top names: Alpha Baltic Media (88), KPMG Baltics (81), PR Kvadrāts (70), PwC (63), EIROEKSPERTS (59), EY Baltic (57), Interbaltija (48), Skrivanek (45)

This is a long-tail / oligopolistic-core pattern. Worth a short paragraph: a small set of incumbents shoulder most repeat participation, while a large fringe enters once and exits — consistent with the persistence-equilibrium story in §1.4 (#J99GB8).

(d) Disqualification is rare in KIBS. Across all CPV 72/73/79 tenders with a disqualification rate coded, mean disq rate is 0.4–1.0% and only 1.4–2.9% of tenders have any disqualification. The “any_disqualification” indicator (variable dictionary, Gate 3) will have very little variance and is unlikely to load in the regression. Suggests integrity risk is operating through expectation/perception rather than observed disqualification — exactly the kind of Gate 1 perception variable the qualitative phase needs to capture, since the administrative trace is small.

Three concrete edits I’d make to the draft

1. #U57A7C (selected paragraph). The dichotomy “if procedural complexity is the binding constraint, simplification helps; if it lies elsewhere, threshold elevation may not” is well-framed. The procedure-type contrast in #ET42TV plus the time-to-prepare gradient I just ran together provide direct preliminary evidence that procedural complexity is not the dominant constraint. Consider tightening the paragraph by foreshadowing this: “The empirical analysis in §2.3 shows that simpler procedures (small procurements) attract fewer bidders than open tenders, suggesting the binding constraint is not procedural complexity in the narrow sense the reform targets.” That makes the introduction’s stakes argument empirically grounded rather than rhetorical.

2. #W66GFF (variables). Insert a candid coverage table or footnote showing which IVs are usable at full sample versus restricted to the ~85-tender extraction subset. Reviewers will ask. The paragraph currently lists ten variables as if they entered uniformly; in practice contract value, price weight, ex-ante deliverable, and amendment count are sub-100-observation variables.

3. #82JSLX (qualitative design). Strengthen the case that the qualitative phase is necessary, not just complementary, given that (i) cross-border participation has no quantitative footprint in the bid-level data and (ii) Gate 3 disqualification has near-zero observed variance. The qualitative phase carries Proposition 3 alone.

Two analyses I would queue next if extraction can be completed

• MEAT × Gate 2 interaction once award_method_price_only is re-extracted from nolikums rather than defaulted. The theoretical prediction in #AN2Q5R hangs on this.
• Value-tier × PTC interaction once bid_ceiling_value is filled from nolikums (planned contract value clause). Without this Proposition 1 has no test.

Tell Jāzeps the data infrastructure is workable, the descriptive baseline is solid, but the regression chapter as currently sketched is blocked on three extraction fields: bid_ceiling_value, price_weight, and a re-validation of award_method_price_only against the population sheet’s evaluation method strings.

Got enough. Here’s the ELI5:

The thesis in plain English

The puzzle. Latvia spends ~15% of GDP through public procurement, but ~22% of tenders only get one bid (vs. ~14% EU average). Each missing bidder costs real money (2.5–6% per extra competitor on price). The Latvian government is reforming the Public Procurement Law in 2026 — most visibly, raising the regulated threshold from €10k to €143k. But the reform is essentially a guess about why suppliers don’t bid. Jāzeps’s thesis asks the prior question: why do legally eligible suppliers stay away, especially in knowledge work (IT, consulting, R&D, analytics)? (#DWCV8W, #U57A7C)

The theoretical move. He reframes a tender as a signaling environment: the buyer designs a screening architecture, and submitting a bid is a costly signal. The supplier’s bid/no-bid decision is then a rational response to perceived transaction costs (PTC) that hit before any contract is awarded. He decomposes PTC into three components: bid preparation burden, evaluation uncertainty, and integrity risk — and builds a “two-gate” participation model (Gate 1 = is this tender even worth engaging with; Gate 2 = can I produce a competitive bid at acceptable cost). Knowledge services amplify all of this because the bid itself requires expensive intellectual labour you can’t recover if you lose. (#YFYYHE, #3T92HU)

The empirics. Sequential explanatory mixed-methods (#U733DL):

1. Quantitative phase — IUB open procurement data 2024–2025, KIBS tenders identified by CPV codes (72/73/79) plus a Latvian-language keyword dictionary as robustness check. Regressions (Poisson/NB for bidder counts, logit for single-bid). The trick: he uses residuals diagnostically. Where the model explains low participation well → cost-binding sub-market. Where actual participation is way below predicted → something the observable variables can’t see (trust, evaluation competence concerns) — and those become the targets for interviews. (#44QV8L, #6CWVF5)
2. Qualitative phase — semi-structured interviews with suppliers (domestic + international, active + withdrawn), using anonymised tenders from the dataset as vignettes, coded against the gate framework. (#82JSLX)
3. Synthesis — a sub-market × binding-constraint matrix (cost-binding / quality-binding / integrity-binding / mixed), used to predict which sub-markets the 2026 reform will actually reach. The reform mostly simplifies formal procedural cost; if a sub-market is informally constrained (trust, integrity), threshold changes won’t help. (#AG5337)

Cross-cutting variable: supplier origin. Cross-border participation in Latvian KIBS is 1.9% vs. Estonia’s 13.6% — same EU rules, very different behaviour, suggesting informal participation costs. (#5M8N7W)

The LLM usage

This is in §3.2 (research design) and is fairly disciplined. The IUB export gives titles and basic fields, but the real gate-model variables (evaluation criteria weights, qualification threshold specificity, documentation complexity, required annexes, award records) live inside PDFs and .doc files, mostly in Latvian, with formatting that varies by contracting authority. Manual coding the full sample (~1,789 tenders) is infeasible, so he uses Claude Haiku via API for structured extraction.

The protocol has four stages (#8LUL7H, #QTUWVU):

1. Manual gold-standard set — he hand-codes a reference set first.
2. Prompt + schema development — Claude Haiku with structured output schemas, iterative prompt refinement against the gold standard. He cites Lunn et al. 2025 for the iteration method, and Konet et al. 2024 / Schmidt et al. 2024 as evidence that LLMs can hit ~80–96% accuracy on structured extraction when guided by a predefined schema and validated. (#C7RMKD)
3. Full pipeline run with audit logging.
4. Validation on a random 20% subsample against source documents, with agreement rates reported per variable (Krippendorff’s principle: reliability isn’t a single number, it varies by what you’re extracting). Variables below threshold are either re-prompted or dropped with the limitation flagged.

He’s explicit about the measurement-uncertainty caveat in the limitations (#NUMYCT). The framing is conservative: LLM as a force-multiplier on a Krippendorff-style content-analysis protocol, not as an oracle — gold standard first, schemas constrain output, human validation per variable, and the methodological literature he leans on is the recent computational-content-analysis wave rather than anything hand-wavy.

So in one breath: theory = tenders are screening architectures, non-participation = rational response to perceived transaction costs; method = mixed-methods with regression residuals targeting the interview phase; LLM role = scaling structured variable extraction from Latvian tender PDFs under a validated content-analysis protocol; payoff = a sub-market typology that predicts which slices of the 2026 reform will actually move participation.