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EPJ Data Science

Table A.4 Estimates for the small model—consolidated Data

From: Corruption red flags in public procurement: new evidence from Italian calls for tenders

 

OLS

LASSO

Ridge

 

Corruption risk

Corruption risk

Corruption risk

Design-Build

0.002

0.002

0.002

[0.004]

  

Urgency

−0.004

−0.004

−0.004

[0.003]

  

Negotiated

0.001

0.000

−0.001

[0.004]

  

Negotiated-No Tender

0.003

0.002

0.002

[0.003]

  

Price Only—w. ABA

−0.003

−0.006

−0.006

[0.004]

  

Scoring Rule (MEAT)

0.007∗

0.007

0.007

[0.004]

  

Open Tender Days

0.001

0.002

0.002

[0.004]

  

Open Tender Day V.

0.006

0.006

0.006

[0.004]

  

Observations

15,818

15,818

15,818

Adj R2

0.033

  

MSE

0.355

0.127

0.127

False Positive

4399

4358

4322

False Negative

2726

2730

2737

Precision

0.223

0.225

0.225

Recall

0.317

0.316

0.314

Threshold

0.189

0.189

0.187

  1. ∗ p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01. All specifications include year and region fixed effects. Robust standard errors in parentheses for OLS estimates. Due to the limited number of observations in our sample, LASSO and Ridge regressions are evaluated through a standard k-fold cross-validation method (with k = 10), and not through the more common train-test split. MSE is equal to the root mean squared error for OLS, and to the minimal cross-validation mean squared error for LASSO and Ridge regressions. False Positive indicates the number of cases in which a non-corrupt firm is classified as corrupt by the model. False Negative indicates the number of cases in which a corrupt firm is classified as non-corrupt by the model. Threshold indicates the predicted value of the outcome variable for which a firm is classified a corrupt.
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