Skip to main content

Does Institutional Quality Matter for Infrastructure Provision? A Non-parametric Analysis for Italian Municipalities


This study explores the relationship between different dimensions of regional institutional quality and the efficient provision of transport infrastructure. A two-stage semi-parametric approach is applied to a large sample of public works procured by about 1700 Italian municipalities in the 2000–2014 period. First, we estimate the performance in contract execution; then, we test the impact of different measures and dimensions of institutional quality at both regional and provincial level. The results provide evidence that the quality of institutional environment matters in infrastructure procurement, though some specific dimensions of institutional quality appear to be more relevant than others in affecting performance in contract execution. Overall, the estimates are robust to alternative measures of institutional quality, alternative model specifications, and different sample selections.

This is a preview of subscription content, access via your institution.

Fig. 1

Source: our elaboration on data provided by Nifo and Vecchione (2014)

Fig. 2

Source: our elaboration on data provided by Nifo and Vecchione (2014)

Fig. 3

Source: our elaboration on data provided by .Charron et al. (2014) and (2015)

Fig. 4


  1. The Organization for Economic Co-operation and Development (OECD) estimates that public procurement accounts for 29% of general government expenditure; 63% of total procurement spending across OECD countries pertains to state and local governments (OECD 2015).

  2. A survey of the literature on the relationship between infrastructure and growth is offered by Estache and Fay (2007), who point that the relevance of infrastructure varies across countries and over time.

  3. Furthermore, a debauched environment can also lead to lower levels and quality of infrastructure (Tanzi and Davoodi 1998; De la Croix and Delavallade 2009).

  4. According to the Indicator of Infrastructure Endowment provided by the Tagliacarne Institute (Istituto Tagliacarne) in 2012 (the most recent data available), there is a marked heterogeneity in the infrastructure endowment across Italian regions, with some regions presenting an endowment higher than the Italian average (e.g., Liguria, Lazio and Tuscany) while others being consistently below the average (e.g., Basilicata, Molise and Sardinia). More detailed information on the infrastructure endowment of Italian regions can be found at

  5. For a detailed review of this literature, see Guccio et al. (2012a).

  6. The extent of such an opportunistic behaviour depends on the incentives involved by the type of contract (e.g., fixed-price or cost-plus contracts) as well as by the selection procedure (e.g., open or negotiated procedures).

  7. A review of this literature is provided by Guccio et al. (2017).

  8. Both indices are described in details in the following section.

  9. For a comprehensive survey of DEA applications, see Emrouznejad and Yang (2018).

  10. For the analytical formulation of the input-oriented DEA model, see Appendix B.

  11. However, some major issues remain regarding the use of asymptotic results and bootstrap: first, the high sensitivity of non-parametric approaches to extreme value and outliers; second, the way to allow stochastic noises in a non-parametric frontier (Simar and Wilson 2008). Another common problem is given by the dimensionality space (i.e. number of input and output variables included in the efficiency analysis) and by the reliability of the results obtained through the DEA model. In the next Sections we explain in detail our empirical strategy to avoid these limitations.

  12. See Simar and Wilson (2008) for technical details on the bootstrap procedures.

  13. See Sect. 4 and Simar and Wilson (2008) for a more detailed discussion of this point.

  14. In Appendix B, we report the detailed procedure followed in the second-stage regression, largely based on Simar and Wilson (2007).

  15. Since 2014, AVCP has been transformed in the Anticorruption Authority (ANAC).

  16. This dataset, however, does not include municipalities that did not communicate data to the AVCP or with incomplete records. Below, we discuss the implications for our results of a possible self-selection bias due to the sample of municipalities in the data. Furthermore, the dataset does not include public works that were not completed by the year 2014, which would have represented an interesting source of information for our research.

  17. For further details on the construction of the IQI synthetic index and its sub-indices, their interpretation and availability visit

  18. For brevity, the geographical distributions of the IQI five pillars are not discussed in the text, but are depicted in Appendix A for the round 2012 (see Fig. 5).

  19. Results can be obtained by the authors upon request.

  20. In Appendix A (see Fig. 6), we show the frequency of DEA CRS efficiency estimates and the kernel density estimates (based on 1000 simulations). The distribution of the DEA CRS efficiency estimates (left-hand side) shows a certain degree of variability across the DMUs. On the right-hand side of the figure, the two kernel density functions indicate that the efficiency scores are similar (i.e. the two tracks are practically over-imposed), with minor changes and fewer fluctuations due to the extra estimation tasks required by the bias-corrected procedure. Bootstrap-corrected efficiency estimates have been run by the package FEAR 1.15 in R (Wilson 2008).

  21. The results reported in Table 3 show that, on average, each contracting authority can reduce both actual time and costs proportionally by only 15.11%, given the target value (that is, the time and costs agreed on in the contract). However, it is important to stress that the fully efficient observations (i.e. those on the DEA frontiers) are not necessarily the ones that fulfil simultaneously time and cost efficiency and that the relatively high (average) efficiency scores do not mean that public contracts for roads in Italy are overall executed in an efficient way. In fact, in the input-oriented CRS model (Charnes et al. 1978) employed here, the efficiency score measures the radial contraction in the actual achievements of cost and time objectives needed to attain the contract target in relative terms. Thus, it identifies the best performing DMUs, in the relevant trait of the bi-dimensional frontier, as the ones that minimize the “distance” of actual achievements from the targets. This implies that the best-performing DMUs could still exhibit a relatively inefficient performance in one of the targets (i.e. time and costs) agreed on in the contract.

  22. As a further test, Table 10 in the Appendix A reports the conditional distribution of the average DEA CRS bias-corrected efficiency scores by three different levels (i.e. high, moderate and low, based on the related sampling distributions) of each synthetic indicator of institutional quality. Independently from the indicator, the average efficiency scores for each level of institutional quality show only moderate differences. However, considering one indicator at a time, a clear beneficial pattern of higher institutional quality on the performance of contracting municipalities emerges. Different tests proposed in the literature (i.e. the Mann–Whitney test and the equality of means test suggested by Simar and Wilson 2008) are employed to check for significant differences in the DEA efficiency estimates among groups. The results of both the Mann–Whitney’s and Simar and Wilson’s (2008) tests reported in Table 10 confirm that the difference in the average efficiency scores between groups is significant, regardless of the indicator of institutional quality employed in the test.

  23. It is worth noticing that, in principle, the choice of the year of the indicator should (and, indeed, does) not make a big difference, inasmuch as the territorial distribution of institutional quality indicators does not change much over time, as already reported in Sect. 3.2. Nonetheless, in our empirical analysis we have preferred to consider the two available extreme rounds (i.e. 2004 and 2012) of the IQI indices, to provide more robustness of our findings. As a further robustness check, for the subsample of public works awarded in the period 2004–2012, we have also estimated second-stage regressions with time-varying IQI indices, with results fully in line with those reported in the paper. Second-stage regressions with time-varying IQI indices are available upon request.

  24. In Appendix A (see Table 9), we also report the pairwise correlation matrix for the DEA efficiency scores (both original and bias-corrected) and the different indices of institutional quality (both synthetic and sub-indices) employed in the second-stage analysis.

  25. Even if only in terms of indirect benchmarking, it is worth mentioning that our estimates show a degree of association between corruption and efficiency in the execution of contracts that is significantly higher than that found in previous literature (Finocchiaro Castro et al. 2014).

  26. To check the robustness of our results, in Appendix A we reduce the unobserved heterogeneity in the characteristics of the procurer by restricting the second stage analysis to a subsample of contracts (i.e. 2600), procured by small municipalities with up to 15,000 inhabitants (i.e. 1312). These municipalities use a single round voting system instead of the runoff voting system adopted by those with more than 15,000 inhabitants. Overall, the results emerging from Tables 11 and 12 for the indices and sub-indices of institutional quality at the regional level mostly overlap with the previous ones. Then, to highlight the importance of controlling for the different aspects of institutional quality in the estimates, in Table 13 we show that, taken individually, all the employed dimensions are highly significant due to the strong correlation among them.

  27. The implementation of standard costs was required since 1994 (L. 109/1994, article 4), confirmed in the 2006 Code of public works, services and supplies (article 7, letter 4, letter b), cancelled by the 2016 revision of the Code and recently reintroduced (Law 96/2017, article 213, letter 3, letter h-bis). Furthermore, some methodological studies on standard costs were prepared by the ANAC both in 2003 and 2012, but without practical consequences. For an updated picture of Italian public works market, see also ANAC (2017).


  • Adserá A, Boix C, Payne M (2003) Are you being served? Political accountability and quality of government. J Law Econ Organ 19:445–490

    Google Scholar 

  • Alexeeva V, Queiroz C, Ishihara S (2008) “Monitoring road works contracts and unit costs for enhanced governance in Sub-Saharan Africa”, Transport Paper 21. The World Bank, Washington, DC

    Google Scholar 

  • Alt J, Lassen DD (2003) The political economy of institutions and corruption in American states. J Theor Politics 15:341–365

    Google Scholar 

  • ANAC (2017) Il mercato dei contratti pubblici—Lavori, servizi e forniture nel periodo 2012–2016. Autorità Nazionale Anticorruzione, Rome

    Google Scholar 

  • Ancarani A, Guccio C, Rizzo I (2016) The role of firms’ qualification in public contracts execution: an empirical assessment. J Publ Procure 16(4):554–582

    Google Scholar 

  • Asatryan Z, De Witte K (2015) Direct democracy and local government efficiency. Eur J Political Econ 39:58–66

    Google Scholar 

  • Badin L, Daraio C, Simar L (2014) Explaining inefficiency in nonparametric production models: the state of the art. Ann Oper Res 214:5–30

    Google Scholar 

  • Bajari P, Tadelis S (2006) Incentives and award procedures: competitive tendering vs negotiations in procurement. In: Dimitri N, Piga G, Spagnolo G (eds) Handbook of procurement. Cambridge University Press, New York, pp 121–140

    Google Scholar 

  • Bajari P, McMillan R, Tadelis S (2009) Auctions versus negotiations in procurement: an empirical analysis. J Law Econ Organ 25:372–399

    Google Scholar 

  • Baldi S, Vannoni D (2017) The impact of centralization on pharmaceutical procurement prices: the role of institutional quality and corruption. Reg Stud 51:426–438

    Google Scholar 

  • Baldi S, Bottaso A, Conti M, Piccardo C (2016) To bid or not to bid: that is the question: public procurement, project complexity and corruption. Eur J Polit Econ 43:89–106

    Google Scholar 

  • Bandiera O, Prat A, Valletti T (2009) Active and passive waste in government spending: evidence from a policy experiment. Am Econ Rev 99:1278–1308

    Google Scholar 

  • Banerjee A, Hanna R, Mullainathan S (2012) Corruption. In: Gibbons R, Roberts J (eds) Handbook of organizational economics. Princeton University Press, Princeton

    Google Scholar 

  • Banker RD (1996) Hypothesis tests using data envelopment analysis. J Prod Anal 7:139–159

    Google Scholar 

  • Bird RM (1995) Decentralizing infrastructure: for good or for ill? In: A. Estache (ed) Decentralizing infrastructure. Advantages and limitations, pp 22–51, World Bank Discussion Paper n. 290, Washington, DC: The World Bank

  • Cavalieri M, Guccio C, Rizzo I (2017) On the role of environmental corruption in healthcare infrastructures: an empirical assessment for Italy using DEA with truncated regression approach. Health Policy 121(5):515–524

    Google Scholar 

  • Cavalieri M, Guccio C, Lisi D, Pignataro G (2018) Does the extent of per case payment system affect hospital efficiency? Evidence from the Italian NHS. Public Finance Rev 46(1):117–149

    Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444

    Google Scholar 

  • Charron N, Lapuente V, Rothstein B (2011) Measuring the quality of government and subnational variation. Report for the European Commission Directorate. Brussels: General Regional Policy Directorate Policy Development. Retrieved from:

  • Charron N, Lapuente V, Rothstein B (2013) Quality of government and corruption from a European perspective: a comparative study of good government in EU regions. Edward Elgar Publishing, Cheltenham

    Google Scholar 

  • Charron N, Dijkstra L, Lapuente V (2014) Regional governance matters: quality of government within European Union member states. Reg Stud 48:68–90

    Google Scholar 

  • Charron N, Dijkstra L, Lapuente V (2015) Mapping the regional divide in Europe: a measure for assessing quality of government in 206 European regions. Soc Indic Res 122:315–346

    Google Scholar 

  • Coviello D, Gagliarducci S (2017) Tenure in office and public procurement. Am Econ J Econ Policy 9(3):59–105

    Google Scholar 

  • Coviello D, Moretti L, Spagnolo G, Valbonesi P (2017) Court efficiency and procurement performance. Scand J Econ.

    Article  Google Scholar 

  • Coviello D, Guglielmo A, Spagnolo S (2018) The effect of discretion on procurement performance. Manag Sci 64(2):715–738

    Google Scholar 

  • Crescenzi R, Rodríguez-Pose A (2012) Infrastructure and regional growth in the European Union. Pap Reg Sci 91:487–513

    Google Scholar 

  • Crescenzi R, Di Cataldo M, Rodríguez-Pose A (2016) Government quality and the economic returns of transport infrastructure investment in European regions. J Reg Sci 56(4):555–582

    Google Scholar 

  • Dal Bó E, Rossi MA (2007) Corruption and inefficiency: theory and evidence from electric utilities. J Public Econ 91:939–962

    Google Scholar 

  • De la Croix D, Delavallade C (2009) Growth, public investment and corruption with failing institutions. Econ Gov 10(3):187–219

    Google Scholar 

  • Decarolis F (2014) Awarding price, contract performance and bids screening: evidence from procurement auctions. Am Econ J Appl Econ 6(1):108–132

    Google Scholar 

  • Decarolis F, Palumbo G (2015) Renegotiation of public contracts: an empirical analysis. Econ Lett 132:77–81

    Google Scholar 

  • Donaldson D (2017) Railroads of the Raj: estimating the impact of transportation infrastructure. Am Econ Rev 108(4–5):899–934

    Google Scholar 

  • Dosi C, Moretto M (2015) Procurement with unenforceable contract time and the law of liquidated damages. J Law Econ Organ 31:160–186

    Google Scholar 

  • Emrouznejad A, Yang GL (2018) A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Econ Plan Sci 61:4–8

    Google Scholar 

  • Estache A, Fay M (2007) Current debates on infrastructure policy. World Bank Policy Research Working Paper n. 4410. The World Bank, Washington, DC,

  • Estache A, Iimi A, Ruzzier C (2009) Procurement in infrastructure. What does theory tell us? World Bank Policy Research Working Paper No. 4994. The World Bank, Washington, DC. Retrieved from:

  • Ferraz C, Finan F (2011) Electoral accountability and corruption: evidence from the audits of local governments. Am Econ Rev 101:1274–1311

    Google Scholar 

  • Finocchiaro Castro M, Guccio C, Rizzo I (2014) An assessment of the waste effects of corruption on infrastructure provision through bootstrapped DEA approach. Int Tax Public Finance 21:813–843

    Google Scholar 

  • Finocchiaro Castro MF, Guccio C, Pignataro G, Rizzo I (2018) Is competition able to counteract the inefficiency of corruption? The case of Italian public works. Economia e Politica Industriale 45(1):55–84

    Google Scholar 

  • Flyvbjerg B (2005) Policy and planning for large infrastructure projects: Problems, causes, cures. Policy Research Working Paper No. 3781. The World Bank, Washington, DC. Retrieved from:

  • Flyvbjerg B (2014) What you should know about megaprojects and why: an overview. Project Manag J 45:6–19

    Google Scholar 

  • Flyvbjerg B, Skamris HM, Buhl SL (2004) What causes cost overrun in transport infrastructure projects? Transp Rev 24:3–18

    Google Scholar 

  • Ganuza JJ (2007) Competition and cost overruns in public procurement. J Ind Econ 55:633–660

    Google Scholar 

  • Gil R, Marion J (2013) Self-enforcing agreements and relational contracting: evidence from California highway procurement. J Law Econ Organ 29:239–277

    Google Scholar 

  • Goel RK, Nelson MA (1998) Corruption and government size: a disaggregated analysis. Public Choice 97:107–120

    Google Scholar 

  • Golden MA, Picci L (2005) Proposal for a new measure of corruption illustrated with Italian data. Econ Politics 17:37–75

    Google Scholar 

  • Guasch JL (2004) Granting and renegotiating infrastructure concessions: doing it right. The World Bank, Washington, DC

    Google Scholar 

  • Guccio C, Pignataro G, Rizzo I (2012a) Determinants of adaptation costs in procurement: an empirical estimation on Italian public works contract. Appl Econ 44:1891–1909

    Google Scholar 

  • Guccio C, Pignataro G, Rizzo I (2012b) Measuring the efficient management of public works contracts: a non-parametric approach. J Public Procure 12:528–546

    Google Scholar 

  • Guccio C, Pignataro G, Rizzo I (2014) Do local governments do it better? Analysis of time performance in the execution of public works. Eur J Polit Econ 34:237–252

    Google Scholar 

  • Guccio C, Lisi D, Rizzo I (2017) Institutional and social quality of local environment and efficiency in public works execution. In: Thai KV (ed) Global public procurement theories and practice. Springer International Publishing, Berlin, pp 199–212

    Google Scholar 

  • Guccio C, Lisi D, Rizzo I (2019) When the purchasing officer “looks the other way”: on the waste effects of debauched local environment in public works execution. Econ Gov.

    Article  Google Scholar 

  • Haggard S, Tiede L (2011) The rule of law and economic growth: where are we? World Dev 39:673–685

    Google Scholar 

  • Hessami Z (2014) Political corruption, public procurement, and budget composition: theory and evidence from OECD countries. Eur J Polit Econ 34:372–389

    Google Scholar 

  • Heywood P (1997) Political corruption: problems and perspectives. Political Stud 45:417–435

    Google Scholar 

  • Iimi A (2009) Infrastructure procurement and ex post cost adjustments evidence from ODA- financed road procurement in Africa, mimeo. Retrieved from:

  • Kaufmann D, Kraay A, Mastruzzi M (2010) The worldwide governance indicators: a summary of methodology, data and analytical issues. World Bank Policy Research Working Paper No. 5430. World Bank, Washington, DC. Retrieved from:

  • Kenny C (2009) Transport construction, corruption and developing countries. Transp Rev 29:21–41

    Google Scholar 

  • Lasagni A, Nifo A, Vecchione G (2015) Firm productivity and institutional quality. Evidence from Italian industry. J Reg Sci 55:774–800

    Google Scholar 

  • Lederman D, Loayza NV, Soares RR (2005) Accountability and Corruption: political institutions matter. Econ Politics 17:1–35

    Google Scholar 

  • León C, Araña J, León J (2013) Correcting for scale perception bias in measuring corruption: an application to Chile and Spain. Soc Indic Res 114:977–995

    Google Scholar 

  • Lewis G, Bajari P (2011) Procurement contracting with time incentives: theory and evidence. Q J Econ 126:1173–1211

    Google Scholar 

  • Mohmand YT, Wang A, Saeed A (2017) The impact of transportation infrastructure on economic growth: empirical evidence from Pakistan. Transp Lett 9:63–69

    Google Scholar 

  • Moretti L, Valbonesi P (2015) Firms’ qualifications and subcontracting in public procurement: an empirical investigation. J Law Econ Organ 31:568–598

    Google Scholar 

  • Nannicini T, Stella A, Tabellini G, Troiano U (2013) Social capital and political accountability. Am Econ J Econ Policy 5:222–250

    Google Scholar 

  • Nifo A, Vecchione G (2014) Do institutions play a role in skilled migration? The case of Italy. Reg Stud 48:1628–1649

    Google Scholar 

  • Organisation for Economic Co-operation and Development (OECD) (2015) Government at a glance 2015: Procurement data. OECD, Paris

    Google Scholar 

  • Rodríguez-Pose A, Garcilazo E (2015) Quality of government and the returns of investment: examining the impact of cohesion expenditure in European regions. Reg Stud 49:1274–1290

    Google Scholar 

  • Rose-Ackerman S (1975) The economics of corruption. J Public Econ 4:187–203

    Google Scholar 

  • Sahoo P, Dash RK (2012) Economic growth in South Asia: role of infrastructure. J Int Trade Econ Dev 21:217–252

    Google Scholar 

  • Simar L, Wilson P (1998) Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Manag Sci 44:49–61

    Google Scholar 

  • Simar L, Wilson P (2000) Statistical inference in nonparametric frontier models: the state of the art. J Prod Anal 13:49–78

    Google Scholar 

  • Simar L, Wilson P (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econ 136:31–64

    Google Scholar 

  • Simar L, Wilson P (2008) Statistical inference in nonparametric frontier models: recent developments and perspectives. In: Fried HO, Knox Lovell CA, Schmidt SS (eds) The measurement of productive efficiency and productivity growth. Oxford University Press, New York, pp 421–521

    Google Scholar 

  • Simar L, Wilson P (2011) Two-stage DEA: caveat emptor. J Prod Anal 36:205–218

    Google Scholar 

  • Søreide T (2014). Drivers of corruption: a brief review. World Bank Policy Research Working Paper No.91642. The World Bank, Washington, DC. Retrieved from:

  • Spagnolo G (2012) Reputation, competition, and entry in procurement. Int J Ind Organ 30:291–296

    Google Scholar 

  • Sweis G, Sweis R, Hammad AA, Shboul A (2008) Delays in construction projects: the case of Jordan. Int J Project Manag 26(6):665–674

    Google Scholar 

  • Tanzi V, Davoodi H (1998) Corruption, public investment, and growth. IMF Working Paper 139, pp 41–60

  • Transparency International (2006) Handbook for curbing corruption in public procurement. Berlin

  • Vidoli F, Canello J (2016) Controlling for spatial heterogeneity in nonparametric efficiency models: an empirical proposal. Eur J Oper Res 249(2):771–783

    Google Scholar 

  • Wilson PW (2008) FEAR: a software package for frontier efficiency analysis with R. Socio-Econ Plan Sci 42(4):247–254

    Google Scholar 

  • World Economic Forum (2016) The global competitiveness report 2016–2017. The World Economic Forum, Geneva. Retrieved from:

Download references


We wish to thank two anonymous referees for their careful review, and the managing editor, Professor Roberta Rabellotti, for her helpful advice. The usual disclaimers apply.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Calogero Guccio.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Appendix A

This Appendix reports some further statistics and robustness checks of the empirical findings obtained in the paper (Figs. 5, 6 and Tables 9, 10, 11, 12, 13).

Fig. 5
figure 5

Source: our elaboration on data provided by Nifo and Vecchione (2014)

Pillars of IQI_ REGION_2012 at NUTS 2 level

Fig. 6
figure 6

Frequency of DEA CRS efficiency estimates (on the left) and kernel density estimates of DEA CRS and DEA CRS bias corrected efficiency estimates (on the right). Source: our elaboration on data provided by AVPC. Note: Figures show the frequency and the kernel density estimates of DEA efficiency scores under CRS assumption, respectively. DEA CRS bias corrected scores are estimated with the procedure proposed by Simar and Wilson (2000). The kernel density functions for the efficiency of contracts for roads are derived from bias corrected DEA efficiency scores using a univariate kernel smoothing distribution and the appropriate bandwidth (Simar and Wilson 2008)

Table 9 Pairwise correlation matrix
Table 10 Conditional distribution of average DEA CRS bias corrected efficiency estimates
Table 11 Robustness check with institutional quality index at NUTS 2 level—semiparametric bootstrap truncated estimates, sub-sample of small municipalities
Table 12 Robustness check with institutional quality pillars at NUTS 2 level– semi-parametric bootstrap truncated estimates, sub-sample of small municipalities
Table 13 Estimates using pillars one by one—semi-parametric bootstrap truncated estimates, full sample

Appendix B

This Appendix reports the analytical formulation of the DEA model. To facilitate the interpretation of the results reported in the paper, the formulation of DEA is presented. A production process using the input vector \( \left\{ {x = x_{i} , i = 1, \ldots ,n} \right\} \in \Re_{ + }^{N} \) is considered to produce an output vector \( \left\{ {y = y_{s } , s = 1, \ldots , m} \right\} \in \Re_{ + }^{M} \). The production process is constrained by the production possibility set \( \varPsi \), which is the set of physically attainable points (x, y) given by:

$$ \varPsi = \left\{ {(x, y) \in \Re_{ + }^{N + M} | = \left( {x,y} \right)\,\, {\text{is feasible}} } \right\} $$

The efficiency of a generic DMU (i.e. a public work contract) is measured by the distance between the observed input-output mix and the optimal mix located on the frontier of \( \varPsi \), which is the boundary of optimal production plans.

The single DMU efficiency score is:

$$ \lambda \left( {x,y} \right) = \inf \left\{ {\lambda |\left( {\lambda x,y} \right) \in \varPsi } \right\} $$

where a value of \( \lambda \left( {x,y} \right) < 1 \) indicates the radial distance of the DMU from the full efficient frontier and a value of \( \lambda \left( {x,y} \right) = 1 \) means that the DMU is efficient. Being \( \varPsi \) the frontier and \( \lambda \left( {x,y} \right) \) unknown, they should be estimated from a sample of i.i.d. observations \( {\mathcal{X}}_{n} = \left\{ {\left( {x_{i} ,y_{i} } \right),i = 1, \ldots ,n} \right\}. \)

The DEA non-parametric estimator assumes the convexity of the hull and, thus, under the hypothesis of constant returns to scale (CRS), can be defined as:

$$ \widehat{\varPsi }_{DEA} = \left\{ {\left( {x,y} \right) \in {\mathbb{R}}_{ + }^{N + M} |y \le \mathop \sum \limits_{i = 1}^{n} \gamma_{i} x_{i} ; x \ge \mathop \sum \limits_{i = 1}^{n} \gamma_{i} y_{i} ,for (\gamma_{1} , \ldots ,\gamma_{n} )\, {\rm such that}\, \gamma_{i} \ge 0, i = 1, \ldots ,n } \right\} . $$

A DEA estimator of the efficiency scores can be calculated by replacing the true production set \( \varPsi \) in (2) with the estimator \( \hat{\varPsi }_{DEA} \):

$$ \hat{\lambda }_{DEA} \left( {x,y} \right) = \inf \left\{ {\theta |\left( {\theta x,y} \right) \in \hat{\varPsi }_{DEA} } \right\} $$

where, by construction, \( \hat{\lambda }_{DEA} \left( {x,y} \right) \le \lambda \left( {x,y} \right) \) (Simar and Wilson 2008).

To study the effect of environmental variables (or non-discretionary input in the second-stage analysis, Simar and Wilson (2007) suggest applying a semi-parametric two-step bias-corrected truncated estimator where the unobserved regress and \( \lambda_{i} \). is replaced by its bias-corrected estimate \( \hat{\hat{\lambda }}_{i} \) obtained using DEA with bootstrap and a maximum likelihood truncated estimator. More specifically, the second-stage regression can be summarized as follows:

  1. 1.

    Apply maximum likelihood estimator in a truncated regression of \( \hat{\hat{\lambda }}_{i} \) on \( z_{i} \). to obtain estimates of \( (\hat{\beta },\hat{\sigma }) \), where \( i = 1, \ldots ,n \) is the number of DMUs.

  2. 2

    Repeat the steps from 2.1 to 2.3, L times to obtain b numbers obootstrap estimates of \( \left\{ {(\hat{\beta }^{*} ,\hat{\sigma }_{\varepsilon }^{*} )_{b} } \right\}_{b = 1}^{L} \):

    1. 2.1

      For each DMU \( i = 1, \ldots ,n \), draw \( \varepsilon_{i} \)from the left-truncated \( (1 - z_{i} \hat{\beta }) \)normal distribution;

    2. 2.2.

      Use \( \varepsilon_{i} \)for each DMUs \( i = 1, \ldots ,n \)to calculate fitted DEA scores: \( \hat{\hat{\lambda }}_{i}^{*} = z_{i} \hat{\beta } + \varepsilon_{i} \);

    3. 2.3

      Apply maximum likelihood estimator in a truncated regression of \( \hat{\hat{\lambda }}_{i}^{*} \)on \( z_{i} \). to obtain estimates of \( (\hat{\beta }^{*} ,\hat{\sigma }^{*} ) \).

  3. 3.

    Compute the bias-corrected estimator of \( \hat{\hat{\beta }} \) as well as the percentile bootstrap confidence intervals at a given level of significance using the bootstrap estimates obtained from the previous step \( \left\{ {(\hat{\beta }^{*} ,\hat{\sigma }_{\varepsilon }^{*} )_{b} } \right\}_{b = 1}^{L} \) and the original parameters \( (\hat{\beta },\hat{\sigma }) \).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cavalieri, M., Guccio, C., Lisi, D. et al. Does Institutional Quality Matter for Infrastructure Provision? A Non-parametric Analysis for Italian Municipalities. Ital Econ J 6, 521–562 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Efficiency
  • Non-parametric methods
  • Semi-parametric truncated regression
  • Municipalities
  • Institutional quality
  • Public works contracts

JEL Classification

  • O18
  • D73
  • H57
  • C14