Hjalmarsson (1998), there are many instances in which it makes sense to focus on labor productivity only, rather than to rely on methods which make implicit assumptions on the joint evolution of factor efficiency and this is being increasingly recognized in the recent literature.

Regarding the environmental variables included in the models, service area is an exogenous (scale) operating characteristic of the firm’s environment. When the number and type of customers served can be assumed to be exogenous, it is often also included as a control variable of some sort—often customer density to get to the demographics impacting the sector; the higher the density the higher the productivity. Differences in voltages required by customers may matter and hence the need to model the share of residential vs. non-residential users. On the quality side, it is not unusual for researchers to include a variable representing the security of supply, peak-load demand, technical losses and outages. Finally, cross-country papers also include GNP per capita to control for differences in the socio-economic environment in which firms operate in each country.

An interesting additional feature of this literature is the willingness of some authors to rely on 2-stage processes to assess the impact of the institutional or policy variables. These authors compute the efficiency scores in a first stage. They then rely, in a second stage, on a regression to explain these scores with various types of variables. The econometric models used vary from simple OLS to Tobit or Probit models.

The large set of papers available and their diversity provide a set of useful lessons. The main one may be that there is a clear interest in benchmarking performance in the sector. Benchmarking can be done to simply generate performance rankings. This was common in the first multi-country studies (Whiteman (1995, 1998) or Pollitt (1995)) and continues to be a popular use of the methods including at the country level (Lavado (2004) for the Philippines). Benchmarking can also be done to generate a sense of the scope for tariff reductions in the case of specific tariff revision as is the case for the papers on Argentina, Brazil, Chile, Colombia and Peru where consulting firms of more academic regulators have worked on generating efficiency measures. Finally, benchmarking can be used to promote some form of yardstick competition within countries or across countries as argued by Domah (2004), Estache, Rossi and Ruzzier (2004) or Resende (2002). In general, the publications tell a fairly powerful story. For Latin America, for instance, the efficiency gains achieved are of the order of 5-7% per year. This is not negligible and significantly higher than what was estimated for the gas sector in Argentina for instance.

These benchmarking studies generate a number of other useful results. First, Pollitt (1995) relying on both parametric and DEA techniques as well as second-stage regression to use institutional variables to explain differences in efficiency scores for each segment of the electricity systems as well as the full systems suggests that there were no significant differences for LDCs from the average of other countries in the early part of the 1990s. These results have been globally confirmed by Whiteman (1995, 1998). Relying on a larger set of countries, he finds that efficiency levels in developing countries were very roughly equivalent to those in Australia and Japan, the United States or lower than in Europe but much higher than in Canada or the Asian Tiger economies—Hong Kong, South Korea, Singapore and Taiwan. He also finds that the “baby tigers”— 7