نقش بالقوه مدل هزینه های اقتصادی در تنظیم ارتباطات از راه دور در کشورهای در حال توسعه

What is the efficient cost of providing telecommunications services to a certain area or type of customer? As developing countries build up their capacity to regulate infrastructure monopolies, cost models are likely to prove increasingly important in answering this question, but without information no real answer can be given. In this paper, we will introduce cost models and establish their applicability when different degrees of information are available to the regulator. Reliable and detailed information is generally a scarce good in developing countries, and we establish here the minimum information requirements that a regulator needs to implement a cost proxy model approach, showing that this ‘data constraint’ need not be that binding.

Worldwide privatization of the telecommunications industry and the introduction of competition in the sector, altogether with the ever-increasing rate of technological advance in telecommunications, raise new and critical challenges for regulation. For matters of pricing, universal service obligation (regulation required to boost industry growth in areas not currently served or to maintain provision to areas in danger of losing it) and the like, one of the key questions to be answered is: ‘What is the efficient 1 cost of providing the service to a certain area or type of customer?’ As developing countries move forward with their efforts to build up their capacity to regulate their privatized infrastructure monopolies, cost models are likely to prove increasingly important for several reasons. Firstly, an independent ability for regulators to assess costs can remove information asymmetries from the process of crafting efficient regulation. Secondly, independent cost estimates can increase transparency and may be helpful in reducing the risks of corruption that may exist in designing or reviewing pricing and subsidy policies. Finally, cost models may help in the development of infrastructure buildout policy by identifying cost differences across regions of the country. Costs models deliver a handful of benefits to a regulator willing to apply them, but they also ask for something in advance: information. Without this vital element no answer can be given to the question posed above. In the remainder of this paper we will introduce cost models and establish their applicability when different degrees of information are available to the regulator. We accomplish the latter by running the model with different sets of actual data from Argentina’s second largest city and comparing the results. The paper is organized as follows. Section 2 deals with the proper definition of costs and their measurement. Section 3 presents the FCC model for cost assessment in detail, while Section 4 discusses the data required to implement it. Section 5 concludes.

Cost proxy models are promising regulatory tools, which can be used to assess the efficient cost of providing telephone services. These models could enable the regulator to estimate the forward-looking economic cost of the service without having to rely on detailed cost studies that otherwise would be necessary. This alternative methodology provides a non-discretionary framework within which regulators and firms can discuss with a significant degree of objectivity, and which could provide an independent check on the accuracy of firms’ cost studies. However, lunch is not free: these alternative approaches require much more time and effort in both data collection and preparation as well as the time and effort spent on model design. In general, reliable and detailed information (as required by costs models) is a scarce good in developing countries. In this paper we have established the minimum information requirements that a regulator needs to implement a cost proxy model approach, and we have shown that this ‘data constraint’ need not be that binding. In particular, the HCPM can run with the following inputs. • Census data. Each unit surveyed in the Census (e.g. Census block, or Census block group) should be referenced to a system of coordinates (e.g. latitude and longitude; nevertheless, the model can be modified to work with another system — even with an Excel worksheet — when latitude and longitude coordinates are not available). There should be information on the number of households in each Census unit, and on the number of units in a Census unit group. • Location of wire centers. Each wire center in the study area should be referenced to a system of coordinates (e.g. latitude and longitude; nevertheless, the model can be modified to work with another system — even with an Excel worksheet — when latitude and longitude coordinates are not available). There should be information on the number of lines provided by each of the wire centers. The telephone companies could provide this information. • All other inputs are provided by the model (i.e. factor prices, technologies, etc.), based on information for the US, but can be freely varied by the regulator when this information becomes available in the country. We have also shown that Census data is a good substitute for the individual customer locations, and that the level of aggregation of the Census information does not markedly alter the estimates. Geocoded customer locations may prove difficult for the regulator to obtain, but aggregated Census data is likely to be available in most developing countries, making cost proxy models easier to apply to their particular realities.