Effects of Policy on Competition in Telecom Equipment Markets: Insights from a Multi-Level Multi-Method Study

Abstract

Telecom network equipment markets continue to play a significant role in shaping the global information and communications system. At one level, network equipment influences network performance attributes and has direct bearings on the dynamics of innovation at higher layers of the Internet ecosystem. More strategically, participating in network equipment markets is of vital importance for nations in the present era when the openness of the Internet is challenged by actions of many stakeholders. During the past decade, momentum in global equipment markets has shifted from established players such as Ericsson, Nokia Siemens, Alcatel-Lucent and Cisco to emerging firms such as Huawei. Consolidation and takeovers have strongly affected the number of North American network equipment suppliers and raised the question of whether and how public policy could contribute to preventing further decline.Determining how policy choices affect firm-level competitive advantage in the oligopolistic telecom equipment markets is a complex task. Three major challenges need to be overcome that no single method of analysis can handle well. First, the telecom equipment sector is inherently dynamic with ever-changing technology and market dynamics. Second, data documenting the industry and its main players are heterogeneous, of non-uniform quality, and need to be dealt with at different levels of aggregation (firms, nations) simultaneously. Third, due to endogenous and independent decisions at multiple levels (firms, nations), the determination of future scenarios is enormously difficult. Thus, there is a lack of understanding and of strong inference about whether and how public policies, – including measures affecting research and development (RD 2G, 3G, LTE; and optical networks. Data for these analyses were collected from several sources, including Infonetics, Ovum, and public sector databases. We used several econometric techniques including machine-learning approaches to overcome the challenges of heterogeneous data. We then took the findings of these models to build dynamic simulation models of competition that allow us to relax the constraints of the empirical analysis based on historical data, and study the effects of various policy decisions on outcomes such as market shares and revenues. Our analysis illuminates four sets of lessons: First, we demonstrate the limitations of the available data and its implications for what we can and cannot learn about telecom market even after comprehensive modeling efforts. Second, we discuss how competition has changed in the telecom equipment markets over time and how, despite it being an oligopolistic and overall a fairly stable market, certain firms have exploited competitive advantage by sustained commitment to RD Europe, Middle East, and Africa; Central and Latin America, and Asia Pacific.