Yes — beyond the “gotcha” list, there are several genuine architectural inconsistencies. These are the ones where the theory and the design don’t actually fit each other. I’ll order them by severity.
1. The PTC construct is defined as perceived, then measured as objective
This is the deepest one. In #NUWHP5 you make a hard commitment: “the costs that determine participation … are not the objective transaction costs … but the costs as suppliers perceive them. … This thesis therefore treats perceived transaction cost (PTC) as the actionable construct.” The regression, however, uses tender-side features extracted from administrative data (#VKAXSB, #C7RMKD). Those are objective tender characteristics, not perceptions.
The propositions inherit the slippage. Proposition 1 — “the marginal effect of perceived transaction costs on participation is stronger for lower-value contracts” (#PKJ772) — cannot actually be tested in the quantitative phase as designed, because PTC is not what’s measured. What you can test is whether objective tender features predict bidder count, and then infer perception. The interview phase is exploratory and underpowered for this (#NUMYCT).
Fix: either (a) rename the empirical construct in chapter 3 to “tender-side cost drivers (TCD)” and reserve “PTC” for the qualitative phase; or (b) add a small structured supplier survey that elicits PTC scales for a stratified sample of tenders. Without one of these, the chapter 2 construct and the chapter 3 measurement live in different ontologies.
2. Supplier-level model, tender-level data
The gate model is a supplier × tender decision: supplier i with portfolio P_i, capacity C_i, and prior buyer experience E_ib evaluates tender t (#ST5688, #2CCJRE). The data is tender-level — N bids per tender. You only ever observe the count, never the underlying decisions. This means every supplier-level mechanism (relational K(s), incumbent amortisation, P_compete relative to peers, OC) is collapsed into an aggregate that is consistent with many incompatible underlying decision distributions.
This is the same identification problem that plagues entry-game empirics in IO: you can fit the reduced form, but you cannot recover the structural parameters of an individual decision rule from aggregate counts without strong assumptions (homogeneity, or a known supplier pool). You don’t make those assumptions explicitly anywhere.
Fix: be honest in §3.2 that the quantitative phase tests implications of the gate model (sign and ordering of associations), not the model itself. The model itself is tested only in the qualitative phase. Re-word the propositions in #PKJ772 accordingly.
3. The model’s own sequential structure breaks the regression’s identification
In #BEVQ3V you commit to strict sequentialness: Gate 1 is categorical, and only tenders where Gate 1 passes generate Gate 2 evaluations. You then say (#VKAXSB) “Gate 2 parameters leave observable traces in administrative tender data” and base the regression on that.
But if Gate 1 closes categorically for some tenders, the bidder count for those tenders reflects Gate 1 failure, not Gate 2 features. The regression is fitting Gate 2 coefficients on a mixture of (Gate 1-passing, Gate 2-binding) and (Gate 1-failing, Gate 2-irrelevant) tenders. This is classic sample selection. In a Heckman world you’d model the selection equation. Here the selection equation is exactly Gate 1 — which you’ve declared unobservable in administrative data.
The result: your Gate 2 coefficients are confounded with unmeasured Gate 1 variation, and the size of the bias depends on how prevalent Gate 1 failure is — which is the very thing you’re trying to identify.
Fix: either acknowledge that the regression estimates a reduced-form mixture of Gate 1 and Gate 2 effects (and stop calling them “Gate 2 determinants” as in #3NHBCQ), or use the buyer-history single-bid rate as an instrument for Gate 1 and run a two-stage specification. The current framing is internally inconsistent.
4. K(s) is defined relationally, measured one-sidedly
The K(s) discussion in #ST5688 is your strongest theoretical move: K(s) is relational, a function of the gap between the supplier’s existing signal portfolio and what the tender demands. This generates the incumbent-amortisation story, which is the mechanism behind much of the persistence claim in §2.4.
But the variable dictionary (#W66GFF) measures K(s) from the tender side only — documentation scope, deadlines, annexes. You have no portfolio data on suppliers (their prior wins with this buyer, their certifications, their team CV bank). So the regression cannot test the relational claim; it can only test whether higher tender-side demands correlate with fewer bidders. The very property that distinguishes your K(s) from “generic procedural burden” is empirically invisible.
Fix: you actually can recover part of this from the EIS data — for each supplier in the bidder list, count their prior bids/wins with the same buyer in the prior 24 months. That gives you a buyer-supplier proximity index. Use it as an interaction with tender-side K(s). This would let you test the relational claim directly, and it’s a tractable add to chapter 3.
5. P_screen × P_compete independence is wrong exactly where you most need it right
In #UKRSMT you assert that P_screen and P_compete “multiply because they represent conditionally independent assessments.” They aren’t independent in the case the thesis cares most about. If a supplier perceives low θ (the process is steered), their P_compete depends on who it is steered toward. An incumbent who reads low P_screen reads it as protective (high P_compete); an outsider reads the same low P_screen as exclusionary (low P_compete).
So the same low P_screen multiplies in opposite directions for incumbents and entrants. Your equation #2CCJRE doesn’t have a slot for this. The integrity-risk channel — your most distinctive contribution — is mathematically suppressed by the multiplicative form.
Fix: either condition the P_compete term on supplier type relative to the perceived steering, or replace the multiplicative form with a sum of regime-specific expected returns. A two-line note acknowledging this in #UKRSMT would also help.
6. The persistence/equilibrium claim is not testable in cross-section
§2.4 builds a self-confirming-equilibrium theory of persistent low-competition states (#SPXPVM, #WU7LXG). Equilibrium and persistence are temporal concepts: a state is persistent if it reproduces over time. Your data is two years of tenders treated cross-sectionally. The buyer-history single-bid rate (#VT68CG) is suggestive but it’s a within-period correlation, not an equilibrium test.
So the theoretical chapter promises a market-dynamics story that the empirical chapter cannot deliver. A panel that reads chapters 2 and 3 in sequence will notice.
Fix: narrow the §2.4 claims to “conditions consistent with persistence” rather than “persistent equilibria,” and explicitly frame the buyer-history measure as a persistence proxy with stated limitations. Or extend the dataset to 2018–2025 (this is technically cheap; IUB open data goes back further) and at least add a buyer-level autocorrelation of single-bid rates.
7. Spence’s separating-equilibrium machinery and Akerlof’s unravelling are blended without typing
You use Spence’s K(s) (a separating-equilibrium device — K(s) sorts types) and Akerlof’s adverse selection (a pooling/unravelling device — high-quality suppliers exit) in the same model (#RJDW5Z, #ST5688). These describe different equilibrium states with different reform implications. Spence says: tune K(s) so it separates without over-burdening. Akerlof says: the market may not have an interior equilibrium at all — suppliers exit until only the worst remain.
The thesis treats both as available without specifying which regime applies where. The reform analysis (§4.2) then assumes Spence-type comparative statics (lower K(s) → more participation), but if a sub-market is in Akerlof unravelling, lowering K(s) won’t bring back the exited high-quality suppliers, because their exit is driven by the price/payoff structure, not by K(s).
Fix: add a typology paragraph in §2.4 that distinguishes (a) separating-equilibrium sub-markets with high K(s), where reform helps, from (b) unravelled sub-markets where reform doesn’t reach the exited tier. This becomes part of your sub-market classification in §4.1 and sharpens the reform predictions.
If I had to pick the two that must be patched before defence: #1 (PTC measurement) and #3 (Gate 1 selection breaks Gate 2 regression). Both are visible from the table of contents alone and a careful examiner will land on them within ten minutes of reading.