The aim of this paper is to provide an empirical test of the role of competition in procurement in reducing the effects of corruption. For this purpose, the paper examines whether competition is able to constrain the waste effects of corruption on the efficiency of execution of public works, building on the results provided by Finocchiaro Castro et al. Int Tax Pub Fin 21(4):813–843, (2014). For this purpose, a two-stage analysis is carried out. In the first stage, a non-parametric approach (data envelopment analysis—DEA) investigates the relative efficiency of each public work execution; in the second stage, the determinant factors of the variability of efficiency scores are investigated. Our results show that increasing competition reinforces the negative effects of environmental corruption on public works execution.
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In a different perspective, Di Gioacchino and Franzini (2008), distinguishing two different forms of corruption, i.e. bribery and extortion, show that competition in public administration is effective in reducing extortion while enhancing bribery.
The use of aggregate data in studies regarding public procurement is part of the general approach observed in the majority of empirical studies investigating the relationship between inefficiency and environmental corruption. For instance, Dal Bó and Rossi (2007) study the market of electricity distribution in Latin America countries, finding that when the level of environmental corruption is high, firms tend to be more inefficient in terms of labour use. On the same vein, Abrate et al. (2015) show that environmental corruption significantly increases inefficiency when looking at solid waste collection activities in Italian municipalities. Both studies rely on country-level measures of corruption.
The characteristics of subjective and objectives indicators of corruption are analysed below (Sect. 2.2).
Golden and Picci (2005) present a measure of corruption focusing on the difference between the amounts of physically existing public infrastructure and the amounts of money cumulatively allocated by government to create these public works (of course, the two terms are standardized). If the difference between the two is larger, it means that higher quantity of money is being lost to fraud, embezzlement, waste, and mismanagement; in other words, corruption is greater. The authors are aware that their proposed index also captures some inefficiencies as well as various illegal activities that affects a clear measure of corruption. However, they argue that because inefficiency and corruption vary together in their framework, their index is essentially unaffected by the inclusion of inefficiency.
The relevance of corruption is, of course, part of the more general issue of “quality” of institutions, which has been shown to be a potentially relevant factor for explaining the efficiency of execution of public works (see, among the others, Guccio et al. 2017a). Nifo and Vecchione (2014) develop a multidimensional Index of Quality of Institutions (IQI) and corruption (measured also through the Golden and Picci index) is one of its five components. Guccio et al. (Guccio et al. 2017b) estimate the relevance of corruption within the overall quality of institutions, as measured by IQI, in the explanation of the differences in the efficiency of execution of public works across public procurers. They show that the overall quality of institutions matters, but that only the components related to control of corruption and to government effectiveness are significantly associated with the efficiency of contracts’ execution. The specific policy relevance of corruption in Italy, within the general problem of the quality of institutions, is also shown by the recent updates of the Corruption Perceptions Index by Transparency International, which ranks Italy only 60th (over 176 countries), third to last among European countries.
The efficient management of public works can be obviously assessed alongside other aspects, related, for instance, to the output/outcome of the work (e.g. the quality of the work, its capability of satisfying the needs for which it is been carried out, etc.), which are, however, outside the objective of our work.
For a discussion about the potential drivers of cost overruns and delays see Guccio et al. (2012a).
A possible explanation is that the renegotiation of contracted costs is severely constrained by the law, while no such constraints do exist for delays.
See AVCP—Autorità di Vigilanza sui contratti pubblici di lavori, servizi e forniture (2007).
DEA has been employed in the literature on procurement, also to assess the efficiency of suppliers (see de Boer et al. 2001).
Statistical analysis allows for measuring a central tendency that identifies average performance and the performance of each unit is estimated by deviation from the central tendency.
See Cooper et al. (2007).
For a detailed discussion of perception based measures of corruption see, for example, Kaufmann et al. (2007).
Golden and Picci (2005) find that corruption increases the costs of public infrastructures realisation, especially in the South of Italy.
Namely, the crimes are the ones included in articles 416 and 416-bis of the Italian Criminal Code. It is an objective, aggregated and direct measure that, however, takes into account several crimes, such as embezzlement, extortion and conspiracy (Abrate et al. 2013).
Engineering estimated costs are used as reserve price in tendering procedures.
These public work contracts are among the most commonly procured, representing about a quarter of all public work contracts procured each year. To limit heterogeneity, the public works with a value over 5 million Euros were not included in the sample because of the longer time required to complete mega projects.
In what follows, the observation unit is a single public work contract.
We do not employ other potentially useful controls such as the nature of the contracting authority (e.g., whether local or central) because our focus here is on the relationship between corruption and competition controlling for unobservable regional characteristics with regional fixed effects. For readers interested on this issues we refer to Guccio et al. (2014).
For a detailed discussion of non-parametric frontier estimators and for their statistical properties see, among the others, Simar and Wilson (2008).
More specifically, estimating  with Tobit or OLS regressions leads to the violation of the assumption of the independence between ε i and z i .
However, this approach shows two weaknesses: first, the potential impact of the environmental factors on the distribution of the efficiency scores occurs only if the separability condition is verified (i.e. environmental factors do not influence the shape of the production set). Second, the two-stage approach imposes parametric assumptions on the functional form of the regression and error distribution (Bãdin et al. 2014). In our case it seems reasonable to assume that the employed environmental factors affect the production process but not the attainable set and its frontier.
To measure the level of competition we have not used a variable representing the procedure (whether open or negotiated) because the open procedure is, by large, the mostly used one (almost 86% of contracts are assigned by open procedure). Moreover, the variable we use—number of bidders—is clearly related to the procedure. However, in our empirical analysis, we also tried with other variables connected with the nature of the procedure, as well as with variables that aim at capturing the potential competition in the field (i.e. the number of firms qualified for the specific category of roads at regional level). In both cases these variables have performed very poorly and, therefore, we have preferred not to include them in the main estimates. However we report the estimates using the variable capturing potential competition as an additional test in Sect. 3.5.
To control for sampling variation, we use a bootstrap procedure with 1000 bootstrap developed by Simar and Wilson (1998) to correct the DEA estimate bias, generate confidence intervals and control for sampling variation.
The efficient observations are not necessarily the ones that simultaneously achieve time and cost efficiency.
More than 25% of the contracts have a level of inefficiency between 10% and 60% and about the 75% of contracts has a level of inefficiency below 10%, confirming that cost overruns and delays are relevant phenomena.
We built the reported classes according the distribution of public works in our sample. More precisely, we use the following ranges of reserve prices: 150,000–300,000; 300,000–500,000; 500,000–1,000,000; 1,000,000–1,500,000; 1,500,000–2,500,000; 2,500,000–5,000,000.
We introduced fixed time effects by the year of award (YEAR) because our database is time truncated, since it includes the contracts awarded in the period 2000–2004, which were completed by 2005. This might cause a sample selection related to the fact that the works, which have to be completed near the end of the period under consideration, could systematically show lower delays.
Since our bootstrap truncated estimator requires that employed covariates may only affect the distribution of inefficiencies inside the production possibility set but not the attainable set and its frontier (Simar and Wilson, 2011), this parsimonious strategy tries to control for this potential bias in the estimates. In Sect. 3.5, a further series of robustness checks is performed.
The only difference, in terms of interpretation of the coefficients, is related to the ones for the variables representing competition (BIDDERS and REBATE) and corruption (CORR_PA and CORR_G&P). In our paper, because of the presence of the interaction terms, the coefficients for competition measure the marginal effect of competition on efficiency of execution of contracts, when the relevant corruption index is equal to zero, and vice-versa.
For the pros and cons of normalization in interaction models, see Jaccard and Turrisi (2003).
However, it should be noted that using centered models we are able to capture the marginal effect of a one-unit increase in environmental corruption (i.e. CORR_PA_C and CORR_G&P_C) when competition (i.e. BIDDERS_C and REBATE_C) is at its mean that is zero by construction. So in principle we are able to better disentangle which of the two opposing effects prevails. Nevertheless, we believe that these considerations do not alter our choice in the use of uncentered models as baseline, due the limitations of normalization in interaction models (Jaccard and Turrisi 2003).
This variable measures the number of firms qualified for the category work roads and for different values at regional level. In this case, we are not able to include regional fixed effects in the estimates. In our sample the variable POT_BIDDER has a mean of 2363.66 and a standard deviation of 1004.67.
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We are grateful to the Guest Editors, Professor Stéphane Saussier and Professor Paola Valbonesi, and one anonymous reviewer for their helpful comments. Usual disclaimers apply.
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Finocchiaro Castro, M., Guccio, C., Pignataro, G. et al. Is competition able to counteract the inefficiency of corruption? The case of Italian public works. Econ Polit Ind 45, 55–84 (2018). https://doi.org/10.1007/s40812-017-0086-5
- Public works contracts
- Non-parametric methods