Abstract
The reformed Italian water and sewerage industry has several distinctive features. It is based on a decentralized structure where local authorities are entitled to define detailed long-term budget plans that claim to provide efficiency incentives to operating suppliers. Unlike previous studies, this paper analyzes the cost efficiency embedded in these budget plans to evaluate the actual capability of local regulators to adequately orientate firm performance. Several panel data cost frontier models were estimated that incorporate diverse specifications for inefficiency and unobserved heterogeneity terms. The results indicate that the decentralized planning mechanism applied in Italian water and sewerage industry regulation failed in fulfilling the declared goal and further highlights that the time-invariant terms are the prevailing source of cost differences, which may conceal a structural component attributable to persistent inefficiency.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Water tariffs were kept low in the past as a result of political, social and historical considerations. In many cases, this hindered the renewal of water and sewerage facilities necessary to reduce water losses (above the average rate of 10–15% considered as physiological) and to limit the stop-and-go drinking-water supply suffered in many Southern regions.
The current regulatory scheme was introduced by law no. 36/94, whereas the general criterion to determine water tariffs is comprised in Decree 1/8/96 (the so-called Metodo Tariffario Normalizzato). For a detailed chronology of the process of institutional change of the Italian water industry, see Goria and Lugaresi (2004).
Capital costs relative to new investments are also considered in the definition of tariffs applied to end users but they are not subject to capping mechanisms. In a reformulation of the price determination system that dates back to 2002, but which is still not enforced, depreciation and return on capital components are also proposed to be included in the capping rule so as to be subjected to a regulatory assessment on efficiency improvement rates.
Each function composing the integrated water system has its own formula. The modeled operating costs are then added up in order to obtain the cost of the integrated service. These expressions are not reported here. For a description of modeled costs in water distribution, see Antonioli and Filippini (2001).
In more detail, if planned operating costs exceed the modeled costs by over 20% in a certain year, they must be reduced by at least 2% (based on the planned operating cost of the year immediately preceding); if planned operating costs are inferior to the modeled costs augmented by 20%, they must be reduced by at least 1%; finally, if planned operating costs are inferior to modeled costs, they must be reduced by at least 0.5%.
The average water loss rate in Italy is between 35 and 40% while wastewater conveyance and treatment services have coverage rates equal to 84 and 70.1% respectively (Utilitatis 2006). The latter two, however, are likely to be overestimated given that they are computed on a basis that does not represent the total population and “equivalent inhabitants”. Such deficiencies explain the extensive investment programs that the water industry is expected to implement in the future.
There are also studies based on DEA approach (see, for instance, Thanassoulis 2000a, b; Tupper and Resende 2004; Coelli and Walding 2006). Although the treatment of exogenous effects due to the operating environment is possible in DEA, it is less straightforward than in parametric methods (for a methodology based on a metafrontier framework see De Witte and Marques 2008). Furthermore, the use of DEA in such circumstances could be subject to problems due to an excessive reduction of the reference set, which would make many firms “unique” and therefore fully efficient. Finally, unlike our study, DEA does not allow setting different temporal patterns of the efficiency term.
The England and Wales water and sewerage industry, composed of ten companies, was privatized on 1989 and was subsequently subjected to price-cap regulation, managed by OFWAT, with price review set at 5 year intervals. The first price review took place in 1994 and proved highly restrictive since it allowed an actual annual average increase of only 1.5% until the next price review.
Prior class probabilities and stochastic cost frontier parameters are estimated simultaneously. Note that the inclusion of determinants of the prior class probabilities in the logit stage is not strictly necessary. When no determinant is included, the logit model will proceed with estimation of prior probabilities based on as many constant terms as the number of latent classes. This is the approach that we followed in our application. We also explored the possibility of including variables representing environmental and governance factors directly into the cost specification or as determinants of prior probabilities. The conditional maximum likelihood procedure, however, showed convergence problems.
The latter is a measure of price also used in several other studies on the water industry based on the cost function approach (see, for instance, Filippini et al. 2008; Fabbri and Fraquelli 2000). Garcia and Thomas (2001) also defined the price of materials as the total cost of materials divided by the water volume distributed. Though one may argue that network length is not a reasonable measure of the quantity of the material input, no better proxies were available given the miscellaneous nature of this input. In any case, the effects of dropping this price from the empirical analysis were investigated. Results are reasonably stable in traditional random effects models but unfortunately convergence problems occurred in TRE model.
Coherently with this approach, the price of non-labor variable inputs were computed for each ATO for only the first year of the plan and then left constant over time.
Only a minor residual cost related to the initial capital stock is sometimes explicit in the budget plans in the form of a concession fee.
First, this procedure was applied in those ATOs that presented more detailed data on the value of other plants and infrastructures. It transpired that the estimated value of the two networks (distribution and sewerage) was always around 80–90% of the whole value of the capital. This allowed us to confidently proceed with this simplification, reconstructing the value of the stock with relatively little data (the price of a new kilometer of network, the average age of the network and the kilometers of network at year one), which was requested directly from the ATOs when not available from the plans.
The governance form may be indicative of different planning strategies by local regulators. We are grateful to an anonymous referee for having raised this point.
Estimating a latent class model requires the a priori specification of the number of classes. Empirical models with more than two classes failed to converge. Therefore we based our analysis on a two-class framework. This seems in our view appropriate as it allows inferring some considerations about the characterization of the two groups especially in terms of the two above-mentioned types of governance.
A latent class model with dummy variables was also tested but failed to achieve convergence.
Since the programming activity enforced by each local authority is based on the specific characteristics of individual catchment areas, one can compute an indicator of cost performance due to heterogeneity as \( e^{{ - \left[ {\overset{\lower0.1em\hbox{$\smash{\scriptscriptstyle\frown}$}}w_i - \min \left\{ {\overset{\lower0.1em\hbox{$\smash{\scriptscriptstyle\frown}$}}w_i } \right\}} \right]}} \). This cost performance indicator compares the environmental conditions of each ATO with the best favorable environmental conditions. Also, it is important to note that—by construction—cost efficiency scores measure the economic performance considering all plans as affected by a common operating environment, whereas cost heterogeneity scores measure the unobserved environment effect considering all local regulators as having the same capability to plan efficiently the integrated water service.
Standard deviation and minimum value are equal to 0.112 and 0.415 respectively.
The per-capita investment index in Class 2 is equal to 1.027, which is 24% higher than the value of Class 1 (0.827).
The choice of time horizon depends on the unbalanced nature of the panel data. In fact, after the twentieth year, the number of observations decreases drastically (from 40 to 28) and therefore comparability of average efficiency scores is lost.
References
Abrate G, Fraquelli G (2007) Domanda e tariffe nel settore idrico italiano: un’analisi di sensitività sui Piani d’Ambito. Economia Pubblica 3(4):119–132
Antonioli B, Filippini M (2001) The use of a variable cost function in the regulation of the Italian water industry. Util Policy 10:181–187
Ashton JK (2000) Cost efficiency in the UK water and sewerage industry. Appl Econ Lett 7:455–458
Aubert C, Reynaud A (2005) The impact of regulation on cost efficiency: an empirical analysis of Wisconsin water utilities. J Prod Anal 23:383–409
Barba D, Caputi P, Cifoni D (1997) Drinking water supply in Italy. Desalination 113(2–3):111–117
Bhattacharyya A, Harris TR, Narayanan R, Raffiee K (1995) Specification and estimation of the effect of ownership on the economic efficiency of the water utilities. Reg Sci Urban Econ 25:759–784
Bhattacharyya A, Bhattacharyya A, Mitra K (1997) Decomposition of technological change and factor bias in Indian power sector: an unbalanced panel data approach. J Prod Anal 8:35–52
Bottasso A, Conti M (2003) Cost Inefficiency in the English and Welsh water industry: an heteroskedastic stochastic cost frontier approach. Economics Discussion Papers 573, University of Essex (UK), Department of Economics
Coelli TJ, Walding S (2006) Performance measurement in the Australian water supply industry: a preliminary analysis. In: Coelli TJ, Lawrence D (eds) Performance measurement and regulation of network utilities. Edward Elgar Publishing, Cheltenham, pp 29–61
De Witte K, Marques RC (2008) Capturing the environment, a metafrontier approach to the drinking water sector. Int Trans Oper Res 16(2):257–271
Fabbri P, Fraquelli G (2000) Costs and structure of technology in the Italian water industry. Empirica 27:65–82
Farsi M, Filippini M, Greene W (2005a) Efficiency measurement in network industries: application to the Swiss railway companies. J Regul Econ 28(1):69–90
Farsi M, Filippini M, Kuenzle M (2005b) Unobserved heterogeneity in stochastic cost frontier models: an application to Swiss nursing homes. Appl Econ 37(18):2127–2141
Farsi M, Filippini M, Greene W (2006a) Application of panel data models in benchmarking analysis of the electricity distribution sector. Ann Public Coop Econ 77(3):271–290
Farsi M, Filippini M, Kuenzle M (2006b) Cost efficiency in regional bus companies: an application of alternative stochastic frontier models. J Transp Econ Policy 40(1):95–118
Filippini M, Hrovatin N, Zorić J (2008) Cost efficiency of Slovenian water distribution utilities: an application of stochastic frontier methods. J Prod Anal 29:169–182
Garcia S, Thomas A (2001) The structure of municipal water supply costs: application to a panel of French local communities. J Prod Anal 16:5–29
Goria A, Lugaresi N (2004) The evolution of the water regime in Italy. In: Kissling-Näf I, Kuks S (eds) The evolution of national water regimes in Europe. Kluwer Academic Publishers, Dordrecht, pp 265–291
Greene WH (2002) Alternative panel data estimators for stochastic frontier models. Working Paper, Department of Economics, Stern School of Business, NYU
Greene WH (2005a) Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. J Econom 126:269–303
Greene WH (2005b) Fixed and random effects in stochastic frontier models. J Prod Anal 23(1):7–32
Jondrow J, Knox Lovell CA, Materov IS, Schmidt P (1982) On the estimation of technical inefficiency in the stochastic frontier production function model. J Econom 19(2–3):233–238
Kraemer RA, Pielen B, de Roo C (2007) Regulation of water supply in Germany. CESifo DICE Rep 5(2):21–26
Malaman R, Cima S (1998) L’economia dei servizi pubblici. Indagine sulle grandezze economiche e strutturali dell’industria dei servizi idrici in Italia. FrancoAngeli, Milan
Muraro G (2008) Water services and water policy in Italy. In: Clini C, Musu I, Gullino ML (eds) Sustainable development and environmental management. Springer, Dordrecht, pp 65–80
OFWAT (1999) Final determinations: future water and sewerage charges 2000–05. Available via OFWAT website. http://www.ofwat.gov.uk/aptrix/ofwat/publish.nsf/Content/finaldeterminations
OFWAT (2004) Future water and sewerage charges 2005–10–Final determinations. Available via OFWAT website. http://www.ofwat.gov.uk/aptrix/ofwat/publish.nsf/Content/pr04fd
Orea L, Kumbhakar SC (2004) Efficiency measurement using a latent class stochastic frontier model. Empir Econ 29:169–183
Pitt M, Lee LF (1981) The measurement and sources of technical inefficiency in the Indonesian weaving industry. J Dev Econ 9:43–64
Saal D, Parker D (2000) The impact of privatization and regulation on the water and sewerage industry in England and Wales: a translog cost function model. Manage Decis Econ 21:253–268
Saal D, Parker D (2001) Productivity and price performance in the privatized water and sewerage companies of England and Wales. J Regul Econ 20:61–90
Saal D, Reid S (2004) Estimating opex productivity growth in English and Welsh water and sewerage companies: 1993–2003. Working Paper RP0434, Aston Business School, Aston University (UK)
Saal DS, Parker D, Weyman-Jones T (2007) Determining the contribution of technical change, efficiency change and scale change to productivity growth in the privatized English and Welsh water and sewerage industry: 1985–2000. J Prod Anal 28:127–139
Sawkins JW (1995) Yardstick competition in the English and Welsh water industry. Util Policy 5:27–36
Schmidt P, Sickles RC (1984) Production frontiers and panel data. J Bus Econ Stat 2(4):367–374
Shleifer A (1985) A Theory of yardstick competition. Rand J Econ 16(3):319–327
Thanassoulis E (2000a) The use of data envelopment analysis in the regulation of UK water utilities: water distribution. Eur J Op Res 126:436–453
Thanassoulis E (2000b) DEA and its use in the regulation of water companies. Eur J Op Res 127:1–13
Tupper HC, Resende M (2004) Efficiency and regulatory issues in the Brazilian water and sewerage sector: an Empirical study. Util Policy 12:29–40
Utilitatis—pro acqua energia ambiente (2006) Blue Book. I dati sul servizio idrico integrato in Italia. Roma, Italy
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Abrate, G., Erbetta, F. & Fraquelli, G. Public utility planning and cost efficiency in a decentralized regulation context: the case of the Italian integrated water service. J Prod Anal 35, 227–242 (2011). https://doi.org/10.1007/s11123-010-0192-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11123-010-0192-0