تجزیه و تحلیل مکانی و اقتصادی برای ارتباطات راه دور: شواهدی از اتحادیه اروپا
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|28472||2009||22 صفحه PDF||سفارش دهید||7559 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Applied Economics, Volume 12, Issue 1, May 2009, Pages 11–32
This paper evaluates the role of a number of determinants of telecommunication services in the European Union. We use a logistic model with spatial covariates to estimate the demand function for telecommunications in the Union. Our results show that different types of interconnections generate diverse estimates for country specific demand. The impact on telecommunications from countries with spatial, economic or social similarities differs based on those characteristics. Omitted variable bias from not modeling spatial interdependence is limited in models under spatial connectivity criteria. This satisfies the statistical inference drawn by previous empirical studies regarding determinants of telecommunications.
Over the last few years, the telecommunications sector has received increasing attention in the economic literature and a large volume of theoretical and empirical work has been published in this area. The telecommunications market has changed to a more deregulated environment (Armstrong 1998) following the rules established by regulatory authorities and the demand for telecommunications services has increased tremendously as a result of the expansion of the economic activities ofmany multinational organizations. This paper focuses on explaining the demand for international telecommunications at a European country level. There are a number of papers in the existing literature that are related to our work. Gatto et al. (1988) model residential demand by developing systems of five interdependent equations, corresponding to alternative ways of placing a call for each state. However, they do not apply a spatial econometric framework and thus their results may suffer significant bias. Interestingly, Christaller (1966) uses the number of telephone stations per person to develop a hierarchy of centers among Southern Germany’s cities in 1963 and illustrate his central place theory (CPT). Green (1955) employs telephone call data to define the common boundary of the hinterlands of New York City and Boston. Various inter-city flows (e.g., migration, commuting, and tourism) have been used to analyze regional settlement structures, uncover central place hierarchies, delineate functional and nodal sub-regions, and identify regional disparities (e.g., core and periphery). The latter regional structure motivates the social correlation of telecommunications. Finally, De Fontenay and Lee (1983) analyze residential calls between British Columbia and Alberta. They find that call duration has an inverse relationship with price (economic factors). All the above results justify the empirical formulation of our model. However, the contribution most closely related to ours is that of Gruber and Verboven (2001) who studied the technological determinants of mobile telecommunication services in the European Union and their analysis provided us with considerable insights of the workings of telecommunications market in Europe. In contrast to Gruber and Verboven (2001) and considering the results of the existing empirical literature, we analyze the determinants of demand for telecommunications and evaluate them using a multidimensional method (spatial econometrics).1 Thus, the contribution of the paper is three-fold: (i) decompose the impact of alternative factors that stimulate the demand for telecommunications, (ii) illustrate the effect of geographic proximity, trade and tourism flows on the demand for telecommunication services per country and, (iii) modify and extend the model of Gruber and Verboven (2001) to correct for multifactor bias of the estimates. We take a more general look (Blonigen et al. 2006) at empirically modeling spatial interactions in demand for telecommunications and ask three fundamentalquestions not yet addressed by the previous literature.2 First, to what extent does omission of spatial interactions bias affect coefficients on the traditional regressor matrix in empirical telecommunications studies? Significant bias would call into question much of the existing empirical work and inference. Second, how are spatial relationships estimated using alternative specifications of connectivity effects (social and economic)? Given the existing literature, an obvious issue to examine is the differences across those criteria and whether their presence affects the results. Finally, we examine the evidence of country specific effects. The described approximation may be viewed as an alternative extension of the framework of technological determinants described in Gruber and Verboven (2001) for the telecommunications services. The remainder of the paper proceeds as follows. Section II discusses the empirical strategy of estimation along with the data. Section III shows the empirical results and illustrates the country-specific effects, whereas Section IV presents some concluding remarks.
نتیجه گیری انگلیسی
In this study, we examine demand models for telecommunications for European countries in the context of spatial econometrics. In fact, because of the geographic heritage of these models, their primary application is to incorporate physical notions of space (distance) into the estimation procedure and to argue that geographically nearby units are linked together. Telecommunications in any country depend on the telecommunications in proximate countries or on similar countries in terms ofeconomic or social characteristics. Such types of interdependences have been largely ignored by the empirical telecommunication literature with only a couple of recent papers accounting for such issues in their estimation. This paper manages to incorporate them in its approximation using data from the European Union. Actually, geographic proximity (spatial effects) has a significant role since it allows for the study of agglomeration spillovers, trade interdependencies (economic effects) emphasize the existence of strong financial relationships across European countries and considerable tourism flows (social effects) show a pattern of human migration across Europe. The latter effects are essential for the analysis of telecommunication models. The most important aspect in spatial econometrics is the definition of the connectivity matrix. We defined the distance weight matrices in three different ways: i.e., a) a binary distance measure of contiguity, b) an inverse distance measure of contiguity and c) a k-neighbors measure of contiguity for k = 6, and we also considered two alternative weight matrices: an economic weight matrix using the volume of trade and a social weight matrix using the flow of tourist to incorporate the economic and social effects. This study finds evidence of important connectivity effects and the results are robust across the different specifications of connectivity matrices. Moreover, they indicate the importance of trade and tourism for telecommunications services in any country. Geographic and other spatial characteristics may have a different impact on internet networks than on mobile or wire line communications networks. The rise of these alternative and competing networks presents a potential problem for our approach. For instance, in the United States there has been a significant decline in measured or metered telecom service –which is being replaced first by flat rates for local and toll calls, and later by bundled service packages– masking the per unit call price. Thus, the emergence and the rapid growth of these alternative networks and payment plans should be evaluated on a parallel basis since the nature of this sector of telecommunications is different from the one studied in this paper. The results we present are innovative for the existing literature of telecommunications. Omitted variable bias is limited in telecommunications models with spatial, economic and social connectivity effects. On the other hand, it is worth noting that we find significant omitted telecommunications variable bias under economic or social criteria. This point is particularly applicable to the few previous studies of spatial effects in empirical telecommunications models.