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This page summarizes the contribution, institutional setting, data, and empirical strategy of the paper.

Contribution

The paper makes three main contributions:

  1. FL as screening marker (H1). We show that frequent losers---firms with a zero win rate and abnormally high participation counts---are a reliable empirical marker for procurement anomalies. FL presence correlates with 4--9% higher prices, and FL firms are 3.5 times more likely to co-participate with CADE-convicted cartelists.

  2. Cover bidding mechanism (H2). We provide evidence consistent with the causal interpretation that FL firms act as cover bidders. A leave-one-out IV yields a 21% price markup (\(F = 396\)), Bajari--Ye tests reject bid independence, and network analysis reveals that the price effect concentrates in competitive markets.

  3. Operational screen. The FL screen requires only participation and outcome data---no bid values---making it deployable by any competition authority with electronic procurement records. We propose a four-step implementation blueprint: Flag, Triage, Investigate, Monitor.

Institutional Background

BEC Platform

The Bolsa Eletronica de Compras (BEC) is Sao Paulo state's centralized electronic procurement platform, used by 1,308 public buying units (PBUs) from 2009 to 2019.

Two procurement modalities are relevant:

Modality Format Key Feature
Convite Sealed-bid Requires minimum 3 bidders; threshold R$ 80,000
Pregao Electronic reverse auction Standard modality; no bidder minimum; real-time bids

Cover Bidding Incentives

  • Convite: the 3-bidder minimum creates direct demand for shill bids---a cartel with fewer than 3 members needs cover bidders to meet the threshold
  • Pregao: real-time observation of bids facilitates Regime 1 (complementary) cover bidding, where cover bidders monitor the auction and submit bids above the designated winner

Conceptual Framework

The cartel chooses the optimal number of cover bidders \(n^*\) to maximize:

\[\Pi(n) = \underbrace{B(n)}_{\text{price gain}} - \underbrace{C(n)}_{\text{coordination cost}} - \underbrace{\theta(n) \cdot F}_{\text{detection cost}}\]

Two Regimes of Cover Bidding

Regime 1: Complementary Regime 2: Coordinated
Bid distribution \(U[\bar{b}, \bar{b}+\delta]\) (wide, above winner) \(N(\mu_c, \sigma_c^2)\) (tight, near winner)
Coordination Minimal (just "show up and lose") Precise calibration required
Testable signature Wide FL bid dispersion Narrow FL bid dispersion

Five Testable Predictions

Prediction Description Test
P1 FL tenders have higher prices OLS / IV regressions
P2 FL tenders have more genuine competitors Non-FL firm count
P3 Regime 1 has wider FL bid dispersion Bid spread CV comparison
P4 FL residuals differ from non-FL residuals Bajari--Ye exchangeability
P5 FL residuals are correlated within tenders Bajari--Ye conditional independence

Data and FL Definition

Sample

Dimension Value
Source BEC (Sao Paulo, 2009--2019)
Tender-items 4.5 million (raw); 1.65 million (analysis sample)
Bids 40 million (bid-level)
Firms 41,000 total; 16,843 always-losers
PBUs 1,308 public buying units
Item types 18,783

FL Definition (Two-Step)

Step 1 --- Always-losers: 16,843 firms with win rate = 0 across all 2009--2019 tenders.

Step 2 --- IQR threshold: Among always-losers, compute median + 1.5 \(\times\) IQR of participation counts \(\approx\) 14 tenders. Firms above this threshold are classified as FL.

Result: 2,735 FL firms (16.2% of always-losers).

FL distribution
Figure 1. Distribution of tender participations among always-loser firms. The dashed line indicates the IQR threshold separating FL firms (right) from non-FL always-losers (left).
IQR identification
Figure 2. IQR identification of frequent losers. The threshold at median + 1.5 x IQR classifies firms to the right as FL.

Treatment variable

losers = 1 if a tender-item has at least one FL participant. FL presence occurs in 4.8% of analysis-sample tenders (79,456 tender-items).

Empirical Strategy

OLS Baseline (H1)

\[y_{igt} = \beta \cdot \text{losers}_{igt} + \mathbf{x}_{igt}' \boldsymbol{\delta} + \alpha_g + \lambda_t + \gamma_k + \varepsilon_{igt}\]

where \(y_{igt}\) is the outcome for tender-item \(i\) in item group \(g\) at time \(t\) and purchasing unit \(k\); \(\alpha_g\), \(\lambda_t\), \(\gamma_k\) are item, year, and PBU fixed effects; errors clustered at item level.

Four specifications: (1) item + year FE, (2) + PBU FE, (3) pregao only, (4) convite only.

Four DVs: log negotiated price, log firms, log bids, log non-FL firms.

Instrumental Variable (H2)

\[Z_{kgt} = \sum_{j \neq k} \mathbf{1}[\text{FL firm active at PBU } j \text{ in group } g, \text{ year } t]\]

The leave-one-out instrument counts FL firms active at other PBUs in the same product market and year. The exclusion restriction requires that FL activity at distant PBUs affects PBU \(k\)'s outcomes only through FL participation at \(k\) itself.

Bajari--Ye Tests

Three-step procedure using bid residuals:

  1. Exchangeability: KS test comparing FL vs. non-FL residual distributions
  2. Conditional independence: pairwise product of FL residuals within tenders (bootstrap \(p < 0.001\))
  3. Fake-groups placebo: random assignment yields null results

Software and Estimation

Component Specification
Language R 4.5+
Fixed effects fixest (OpenMP, 16 threads)
Data data.table + arrow (Parquet format)
Tables modelsummary + kableExtra
Figures ggplot2
Clustering Item level (baseline); PBU and two-way robustness
Pipeline 13 R scripts via 00_master.R (~8 min on 16 cores)