| Research Abstract |
This study aims to investigate the characteristics of the telecommunications industry as a whole from both sides of supply and demand analysis. For this purpose, we composed a simulation model of the this industry.
As for the demand side analysis, we examined the demand model including substitution effects which was developed already with additional data. In this analysis, we tried to test the hypothesis, "The radio paging service suppress the demand for the cellular telephone service through the substitution effect between them." For this objective, we built the more general model which covers cellular telephone, the radio paging service, PHS, fixed access. As the result, we certainly admit the substitution between the 2^<nd> generation of cellular telephone and the radio paging, but as for the 1^<st>st generation of cellular telephone, we can not see the hint of substitution. Therefore, the reason why the cellular telephone market suppressed in early stage is not due to the substituti
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on effect. Rather, this phenomenon can be explained by the cost structure of cellular telephone service of 1^<st> generation itself, and the shift from radio paging to 2^<nd> generation cellular telephone might bring the explosive expansion of the whole cellular telephone market.
This result is important from two points of view. The first, we can not neglect the supply side (tariff or price level) in order to explain and forecast the dynamics of telecommunications market as well as the demand side. The second, it is needed to build the simulation model including both side and analyze its sensitivity in order to add more confidence in estimation.
As for the supply side (the cost structure), we use Data Envelopment Analysis (DEA) besides of estimating the parametric cost function statistically. Though DEA is often used to measure the technical inefficiency of several companies, we can show this method can be applied to the cost analysis including the test of natural monopoly of a certain industry ("The measurement of natural monopoly in cellular telephone market using Data Envelopment Analysis"). However, the results by DEA are different from those by statistical estimation. In DEA literatures, it is often said that DEA does not have sufficient way of treating time series data, and this point might affect the above mentioned problem. In order to improve this situation, we developed a dynamic DEA methodology based on Fare and Grosskopf(1996) and Sengupta(1995).
It can be said that these efforts make possible to build a basic framework of the I-O simulator for telecommunication industry which is the final objectives of this study. Less
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