The restructuring of network industries during the last two decades has typically involved privatization of state- and municipally owned utilities and a change in regulatory regime from cost-of-service, or rate-of-return, regulation to incentive-based regulation. These re-regulatory initiatives have been encouraged by economic theories that predict that private suppliers and competition increase cost efficiency. (1) Such outcomes have in fact been observed in electricity and water distribution (Kumbhakar and Hjalmarsson 1998; Saal and Parker 2000). (2)
Nevertheless, the restructuring seen in different countries varies from complete privatization of multiple utility services and a heavy reliance on cost incentives, in for example England and Wales, to no or only marginal adjustments, exemplified by the water, wastewater, and district heating sectors in Sweden. (3)
The wide variety of adopted policies can be attributed to the empirical difficulty of establishing a unified view of how utility performance is affected by different ownership and regulatory arrangements. The mainstream predictions are questioned by, for example, Kwoka (2005a, 2005b) and Bhattacharyya et al. (1995), who suggest that publicly owned utilities are more cost efficient in electricity and water distribution, respectively, whereas others argue that ownership is only relevant in combination with other market and utility characteristics, such as type of regulation (Aroncena and Waddams Price 2002; Berg, Lin, and Tsaplin 2005) and utility size (Bhattacharyya et al. 1995). In terms of regulation, incentive models have been found to improve welfare (Estache and Rossi 2005) but Goto and Tsutsui (2008) find no effect of deregulatory initiatives in the U.S. electricity distribution, although the precise natures of the pre- and post-regulatory regimes are unclear in their case. (4) Aubert and Reynaud (2005) find that water utilities are more cost efficient under rate-of-return regulation than under price cap regulation. The authors attribute this outcome to the extensive access to information that supplements the particular rate-of-return regime they investigate.
A tentative explanation to why conclusions vary is the specific conditions that prevail in each country and industry; however, the studies referred to above also suggest that ownership and regulation might not affect performance by themselves but rather through combinations with other factors. In addition, behavioral assumptions and model specifications vary, which can potentially inflate differences in empirical outcomes. Bhattacharyya et al. (1995), Berg, Lin, and Tsaplin (2005), Hattori, Jamasb, and Pollitt (2005), Aubert and Reynaud (2005), Pombo and Taborda (2006), and Goto and Tsutsui (2008) all use stochastic frontier (i.e., dual disturbance) specifications, whereas Kwoka (2005a, 2005b) and Saal and Parker (2000) use single disturbance specifications. The choice between dual and single disturbance specifications is related to the assumption of utility objective, because a frontier specification rests on an assumption of cost-minimization while a function through the average of the data assumes that utilities follow any arbitrary objective. If it can be assumed that utilities minimize costs, then frontier methods have superior economic properties because the presence of a frontier is consistent with the behavior of economic optimization with a departure from the frontier interpreted as a lack of ability and/or luck. However, the choice of utility objective is not straightforward and although a majority of the studies surveyed above apply frontier specifications, it has been claimed that a cost-minimizing assumption could be overly restrictive (Kwoka 2005a). It can nevertheless be argued that most incentive-based approaches encourage cost-minimization and they typically compose a double incentive structure to separately reward frontier reductions and penalize inefficiency. …