An Analysis of Structural Change in the Telecommunications Industry

By Szmania, Joe | Review of Business, Spring 1989 | Go to article overview

An Analysis of Structural Change in the Telecommunications Industry


Szmania, Joe, Review of Business


An Analysis of Structural Change in the Telecommunications Industry

Introduction

Enormous changes have occured in the telecommunications industry over the past decade. Obviously, one of the biggest events was the breakup of the Bell System which occured in January 1984. But a number of other changes have also taken place. The emergence of alternative providers and the wholesale introduction of new services have given customers increased choices and flexibility to meet their long distance needs. In addition, technological advancements resulting in such services as integrated services digital network (ISDN) promises to open up new opportunities in long distance provisioning. Moreover, as the structure of the U.S. economy changes, so does the use of telecommunications in the industrial process. Data transmission and the ability of computers to communicate over long distance networks have become increasingly important. As business needs have changed, long distance no longer means simply making a telephone call.

Therefore, from both the buying side and the selling side, it can be seen that the industry is changing. Given this, a natural avenue of inquiry might be to ask if statistical evidence of these changes can be found. In other words, does an analysis of the data indicate that such shifts are occurring and what is the nature of these shifts? Knowledge of this would validate the view that the telecommunications industry is undergoing the large scale transformation that has long been predicted. This information would also be useful from an analytical perspective when studying the various types of statistical and economic relationships that are found in telecommunications. Changes in the nature of these relationships would require that this new information be taken into account when generating forecasts or analyzing various issues that affect the telecommunications industry.

Methodology and Data

A strictly time-series approach was utilized here. That is, no economic or price data were used. The intent was to focus solely on the data series itself and to also take advantage of some recent advances in the analysis of time-series data. The series that was analyzed consisted of quarterly interstate volumes for the entire telecommunications industry. This included all interstate services across all long distance carriers. The series was then converted to total holding time in hundred call seconds (THTCCS). THTCCS is an internal AT&T measure of network utilization which includes such characteristics as call set-up time. For a number of applications, it is considered the most relevant measure of the demand that is placed on a network. The data series ran from 1976:1 through 1987:3.(1)

Since the purpose of this paper was to focus on structural changes in the telecommunications industry, a natural place to start was with divestiture which began on January 1, 1984. Therefore, we first considered a sample running from 1976 through 1983. Given this, the next step was to derive the model specification that best represented the series over that interval. In initial estimations, convergence problems often occurred when including moving-average components in the equation. Consequently, moving-average terms were eliminated from subsequent regressions. When the series was measured in levels, an examination of its autocorrelations suggested that the data was nonstationary. Consistent with this was that the estimation of simple ARIMA models indicated the possibility of unit roots. We then formally tested for the presence of unit roots. In other words, in the simple formulation, (1) Y sub t = a + bY sub t-1 + e sub t we wanted to test the null that b = 1. In this case, conventional asymptotic distribution theory could not be applied. Dickey develops an alternate distribution for b. Under the null hypothesis, it has "tau-distribution" that is not the standard t-distribution. …

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