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Evaluating the efficiency of public procurement contracts for cultural heritage conservation works in Italy

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Abstract

Almost everywhere public intervention in cultural heritage (CH) conservation is widespread. Using Italy as a case study, the paper analyses public capital expenditure for CH conservation and investigates whether the high degree of specialization of contracting authorities affects the efficiency of CH conservation works. A two-stage analysis is carried out. At a first stage, a nonparametric approach (Data Envelopment Analysis—DEA) investigates the relative efficiency scored by each single work; at a second stage, the determinant factors of the scores variability are investigated. The empirical analysis shows that, ceteris paribus, the expertise affects the efficiency of CH works.

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Notes

  1. A possible explanation is that public spending for CH conservation represents a very small share of GDP. Data on total public spending for culture are provided by OECD (2006) and Klamer et al. (2006).

  2. The performance of CH works, as compared to the rest of public works, has been investigated, with a different approach, by Guccio and Rizzo (2013).

  3. For instance, see the Canadian Federal Heritage Buildings Review Office (1996).

  4. A closer analysis of the Authority is provided by Rizzo (2008).

  5. According to the estimates of the Autorità di vigilanza sui contratti pubblici di lavori, servizi e forniture, almost 64 % refers to “restoration” while the others are classified as “maintenance” (24 %), “new intervention” (8 %) and “others” (5 %).

  6. Indeed, there might be an underestimation of the overall public demand for CH conservation for two reasons. On one hand, the figures refer to the final stage of the tender, for example when the winner is chosen, while the number of the tenders just issued might be higher (but data are not available); on the other hand, the above figures are based on the data which each contracting authority has communicated to the Authority, while it is likely that some of them do not fulfil the obligation on time.

  7. The average size for the public works in general is 931,705 euros.

  8. The 49.39 % of the overall public works contracts are awarded by municipalities and 6.14 by central government.

  9. According to the reform of the organization of the Ministry of Cultural Heritage and Activities occurred in 2007 (and revised in 2009), nowadays, the formal responsibility of being contracting authority pertains to the Regional Branches of cultural heritage and landscape (Direzioni Regionali per i beni culturali e paesaggistici). Such a reform, however, does not apply to our analysis since our sample covers the period 2000–2005. Provveditorati are central government contracting authorities operating at interregional level on behalf of other public bodies.

  10. Central government operates also through other contracting authorities, the Provveditorati, acting at interregional level on behalf of other public bodies; however, their activity is of very limited size (only 57 works in the period 2000–2005), and therefore, no specific attention will be paid to them in this paper.

  11. Table 4 reports a small difference in the overall number of contracts, 4.252 instead of 4.997 since the information regarding the in-house project was not available for all the observations.

  12. Restricted procedures are mainly concentrated in the contracts between 150,000 and 500,000 euros.

  13. The system is run by private companies (Società Organismo di Attestazione—SOA); they evaluate whether each firm is entitled or not to obtain the required qualification.

  14. There are 13 general categories, so called OG (such as roads, restoration and maintenance of built heritage, dams, underneath works of arts, railways, etc.) and 34 specialized categories, so called OS (such as, decorated surfaces and mobile heritage, archaeological excavations, telecommunications infrastructures, landscape, etc.).

  15. There are 8 classes ranging from 258,228 up to 15,493,708 euros.

  16. These shares are higher if calculated within the general and the specialized sectors.

  17. Ganuza (2007) provides a rational explanation for what could be regarded as underinvestment in project design. A higher investment on a more accurate initial design lowers the probability of renegotiation and of awarding the project to the most efficient firm, but it increases its rents, when competition is not perfect.

  18. This is the case, for instance, when changes in regulations, affecting the execution of public works, occur after the contract is signed or when unforeseen contingencies require technical changes.

  19. See above Sect. 2.1.

  20. A more comprehensive discussion of the nonparametric efficiency DEA technique is provided in the next section.

  21. Just as an example of applications of DEA, different from traditional estimation of production frontiers, see Førsund and Zanola (2006a, b), who measure auction houses performance, in terms of prices of art objects. For an application of DEA to the analysis of efficiency of execution of public works for highways and roads see (Guccio et al. 2012b).

  22. In the input-oriented CRS model (Charnes et al. 1978) employed here, the distance measures the radial contraction in the actual achievements of cost and time objectives needed to attain the contract target. This approach, however, has problems with the evaluation of the non-radial input slack (Cooper et al. 2007). To overcome this limitation, the non-radial models (SBM, slacks-based measure by Tone 2001) are introduced.

  23. More extensive analyses are provided by Cooper et al. (2007) and Fried et al. (2008).

  24. A detailed survey of these approaches can be found in Simar and Wilson (2008). See also Wilson (2012) for a discussion of these approaches and for a proposed extension of order-m estimator obtained by Cazals et al. (2002).

  25. DEA has been largely applied to measure the efficiency of art and culture organisations such as symphonic orchestras in the United States (Luksetich and Nold Hughes 1997); museums in Italy (Pignataro and Zanola 2001; Basso and Funari 2004) and Spain (Del Barrio et al. 2009); auction houses Førsund and Zanola (2006a, b); heritage authorities (Finocchiaro Castro and Rizzo 2009; Finocchiaro Castro et al. 2011).

  26. As it is mentioned before (see Sect. 3.2), an alternative approach would be to include environmental variables as inputs when estimating the efficiency frontier (Banker and Morey 1986).

  27. A quite different approach was proposed by Balaguer-Coll et al. (2007) that employ in the second stage a mixture of nonparametric smoothing regression and nonparametric density estimation techniques.

  28. See above Sect. 2.1.

  29. The conclusion of each work is officially certified by the procurer.

  30. The sample is obtained dropping the bottom and top centile of the distribution of cost overruns and time delays to overcome the impact of outliers.

  31. Finally, even if the use of a CRS assumption is more appropriate in the context of performance measurement, because it identifies overall inefficiency, it may be possible that the efficient execution of contracts, as defined in this paper, depends on the size of contracts. There are several statistical consistent methods to test for VRS such as Simar and Wilson (2002). However, since our interest is only to investigate for the presence of possible returns to scale, we perform the inspection test suggested by Pedraja-Chaparro and Salinas-Jiménez (1996) and we use a truncated regression approach (Finocchiaro Castro and Guccio 2012). Thus, we have run a truncated regression to analyse the relationship between the efficiency scores (CRS) and the size of the contract, as measured by the reserve price and the variable was not significant. Results can be obtained by the authors upon request.

  32. The Kernel distributions of unbiased inefficiency scores are moved slightly to the left due the bias correction in relation to the original ones.

  33. We also compute Pearson and Spearman’s rank correlation between biased and biased-corrected efficiency scores and rank orders and the result show that, in terms of sensitivity analysis, estimated efficiency scores are robust respect to sampling variation. Results can be obtained by the authors upon request.

  34. There are many nonparametric tests suitable for such comparison that, however, would not take into account the finite sample bias and dependence brought by using DEA (Simar and Zelenyuk 2006).

  35. See Table 9.

  36. All the different works realized within each single contract are attributed to sub-categories, according to their nature. It is reasonable to assume that the higher the number of sub-categories involved in the realization of a work, the more complex it is. Moreover, given a number of sub-categories, complexity may be assumed to be decreasing in the concentration of works in one or few subcategories. Therefore, to capture the complexity of each work, we employ a weighted composition index (WCI). More formally, if \( W_{\left[ i \right]j} \)is the amount of money to be spent, within the j-th public work, with (j;1,…,n), for works of the i-th sub-category (i; 1,…,G), and \( W_{\left[ i \right]j} \ge W_{{\left[ {i + 1} \right]j}} \forall i \), then \( {\text{WCI}}_{j} = \sum\nolimits_{i} i \frac{{W_{\left[ i \right]j} }}{{\sum {W_{\left[ j \right]} } }} \in \left[ {1,\frac{G + 1}{2}} \right] \).

  37. Per capita income is lower in the South than in the North area and in the Centre.

  38. Although because of the sample dimension, multicollinearity is probably not a severe problem we test for pairwise collinearity and find that correlation between independent variables is largely acceptable.

  39. We apply semi-parametric two-stage technique, algorithm 2, in Simar and Wilson (2007).

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Acknowledgments

We would like to thank the participants of XXII SIEP conference held in Pavia on the 20–21 of September 2010, and Victor Fernandez-Blanco, Antonello Scorcu, Roberto Zanola and the participants of Fifth European Workshop on Applied Cultural Economics held in Dublin, Trinity College, 1–3 September 2011, as well as the anonymous referee, for helpful comments and suggestions. We are also grateful to Autorità di Vigilanza sui Contratti Pubblici di Lavori, Servizi Forniture for supplying the data used in the analysis. The usual disclaimer applies.

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  1. Department of Economics and Business, University of Catania, Corso Italia, 55, 95121, Catania, Italy

    Calogero Guccio, Giacomo Pignataro & Ilde Rizzo

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Guccio, C., Pignataro, G. & Rizzo, I. Evaluating the efficiency of public procurement contracts for cultural heritage conservation works in Italy. J Cult Econ 38, 43–70 (2014). https://doi.org/10.1007/s10824-012-9194-2

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