Company Characteristics

Our second-stage analyses were performed to look for patterns in the results of the efficiency potentials due to company characteristics. Hence, if patterns were detected, this could lead to suggested groupings of the companies in the benchmarking process, as it is best to benchmark against similar firms.

7.3.1 Private versus Municipal

The private and municipal analysis was performed to determine if the ownership structure should be considered during the benchmarking process (choice of frontier) of the water companies.

The groupings regarding ownership of the frontier firms showed to have an impact on the efficiency potentials.

The Water Department only allows the municipal water firms to be considered the frontier.

The municipal firms are considered to be less efficient in general. Therefore, we looked at the efficiency potentials when municipal firms were only allowed on the frontier, and efficiency potentials when private firms were allowed on the frontier, as well as efficiency potentials when two separate DEAs were performed; one only with private and one only with municipal water companies. The results indicated that lowest efficiency potentials out of all the options were

provided when municipal firms constitute the frontier. The highest efficiency potentials were gained from allowing private water companies on the frontier. Performing two separate analyses gave a result between the other two approaches, although closest to the results from having the municipal companies constituted the frontier (see Table 9).

It is debatable whether the Water Department’s approach of only allowing municipal water companies on the frontier is the correct approach. The private companies have a lower level of customer service, voluntary workers, etc. that decrease the OPEX compared to municipal firms.

Therefore, we recognize the argument for only allowing municipal firms on the frontier, but we also believe that this rule may not be fair to the consumers. The market has a mix of private and

municipal water firms and the benchmarking should be for the whole market, with a frontier that represents the whole market. Another concern of only allowing municipal water firms constituting the frontier is that some private firms appear efficient, whereas they would prove to be inefficient if private firms are allowed on the frontier.

Therefore, we consider the separate analyses as a fair compromise between the frontier choices, since the water firms are then benchmarked against similar firms. In addition, this appeases the arguments that municipal firms should not be benchmarked against private firms since the OPEX costs will be lower for those firms, but also that private firms should not unnecessarily benefit by only being benchmarked against the municipal firms, leading to lower market savings (see Table 9).

In conclusion, we believe that two separate analyses ideally should be performed as described above, as this is acceptable from a regulatory and theoretical perspective. However, unless that is implemented, we think that private firms should constitute the frontier to grasp the efficiency of the whole water market. With the whole market argument in mind, the remaining analyses were

performed with the private water companies constituting the frontier, unless it was found applicable to use the frontier of municipal companies instead.

7.3.2 Company Size

The size analysis was completed to investigate if the firm’s size influences the efficiency potentials.

The analysis was performed with both the CRS-assumption and VRS-assumption (for water firms allowing private companies on the frontier), to test the theory that VRS takes size into

consideration. It should be noted that the size intervals are subjective, and therefore different results could have been achieved with a different classification. Different results may also have been achieved by using a different category to group the size of the firms. We used FADO since FADO originated from data that was audited and therefore considered the most credible.

For water firms, the size categories showed different efficiency potentials, although the average for each category was within a 10%-points difference of each other under CRS and showed large differences in efficiency potentials under VRS. The greatest change in average efficiency potentials was seen in the very large firms, with a difference of 46%-points. This is not unexpected, as it has been previously established that VRS takes into account size, which is why the efficiency potentials decreased when using VRS. The efficiency potential gradually decreased more coincidently the larger the size category was, leading up to very large water firms’ efficiency potentials decreasing the most.

The efficiency potentials for the sewage firms were fairly consistent regardless of using CRS or VRS, and the categories’ average efficiency potentials were close to each other. The very large sewage firms benefited the most from changing the CRS to VRS with a 10%-point decrease in the average efficiency potential. This did not surprise us since the sewage firms in general are closer in size to each other (see Table 10 and 11).

Looking at results under CRS, which, as earlier mentioned, is the approach taken by the Water Department, company size does not seem to have a large impact on the efficiency potentials.

As mentioned earlier, in the water industry, the average efficiency potentials for each size category are within a 10%-points difference of each other, and this difference is even less within the sewage industry under CRS. The upper and lower boundaries differ in range, although most categories overlap, which indicates robustness.

Clearly, the very large firms gain the most (i.e., efficiency potential decreased the most) when firms are benchmarked against similar-size firms (under VRS). Apart from the scale assumption used as it was discussed in prior analyses, we do not find that the firms’ size impacts the efficiency potentials significantly.

7.3.3 Scale Efficiency

The scale efficiency analysis was executed to determine how many firms are operating at their most productive scale size using CRS, first frontier, allowing private companies on the frontier and a second analysis with only municipal firms on the frontier for water.

For the water companies, it was found that the majority of firms are operating at close to optimal level regardless of the frontier standard (see Figure 11). Clearly, when the frontier allows for private water firms, fewer firms are considered close to optimal level. Regardless of the ownership in regard to the frontier, the majority of the firms considered close to optimal level are private firms.

This result was surprising, as the private firms normally are considered small. However, the result could be affected by the fact that the majority of the water companies are small (See Table 10), and therefore sets the norm.

For the sewage industry, which only has municipal firms, over 80% of the firms were considered to be close to optimal level.

In conclusion, the scale efficiency showed that many firms are operating at close to an efficient level and few are at the optimal level in Denmark. Although, the scale efficiency was subject to model constraints, since our results changed when we changed the frontier’s characteristics (private or municipal).

7.3.4 Regions

For the regional analysis, we examined whether the location of firms influences the efficiency potentials using CRS, with private firms allowed on the frontier using the first band. This analysis was performed with the correction for density in the DEA, as well as a DEA without the correction.

It was found that the density correction decreased the efficiency potential for all the regions.

We believed that if the density correction worked correctly, then the efficiency potentials would decrease mostly in certain regions (e.g., Capital region). Similar results were obtained for the water and sewage firms. Regardless of if the density correction was included or not, the Capital Region had the highest efficiency potentials.

As mentioned earlier, the Water Department allows some cost drivers to specify location (zone) of the cost driver (i.e., the city and inner-city zone categories have the highest coefficients); therefore region specifications have already been implemented in the model before the density correction.

Overall, we find region matters in terms of the results of the Capital region. Therefore, it does not appear that the current density correction used by the Water Department combats the regional influences. As shown in the analysis, the density correction is providing some companies that do not have any inner-city piping to get a higher correction than those firms in the city. We also find that since density is accounted for in some cost drivers, then the density correction should not be needed. Therefore, our conclusion is that we think the model needs to be improved in terms of a density correction that captures costly conditions based on regions.

7.3.5 Economies of Scope

To possibly detect economies of scope, efficiency potentials of the multi-product water and sewage firms were compared to single product firms using CRS with municipal firms on the frontier. Only municipal water companies were allowed on the frontier for this analysis to compare with sewage since all the multi-product firms are municipal. Slight signs of economies of scope were found in the multi-product sewage companies, whereas, a small tendency of diseconomies of scope was found in the multi-product water firms.

We cannot know for certain whether the efficiency potentials are influenced by the economies or diseconomies of scope, or if other factors such as size or region interfere. Therefore, we tried to detect any patterns of such kind (see Table 13 and 14).

It was found that the diseconomies of scope for the multi-product water firms may be due to company size, whereas the signs of economies of scope in the multi-product sewage companies might be influenced by regional placement. It is uncertain whether the economies/diseconomies of scope are affected by the factors mentioned or if it is the other way around.

In general, it is difficult to detect economies of scope with certainty as mentioned in the theoretical section of economies of scope. Numerous studies mentioned in the economies of scope section showed that when economies of scope were found, it would usually be within small companies.

This does not coincide with our results. However, the definition of smaller firm might differ.

We also find it interesting that, as mentioned, in OFWAT the companies on the frontier must be multi-product firms, since OFWAT believes that the costs for these multi-products will be higher.

Although, it is possible that OFWAT allows different costs to be included in the analysis.

Since the multi-product companies of the water industry seem to suffer from diseconomies of scope, and the multi-product companies in the sewage industry seem to benefit from economies of scope, we think that the contradiction in results may be due to different accounting methods and not based on true economies/diseconomies of scope.