How Data Gaps (re)Make Rural Broadband Gaps

Abstract

This paper examines challenges to evidence-based decision-making in the design and implementation of rural broadband investment programs. Our focus is on Canada, but similar challenges are evident in the international literature. Based on proprietary telecommunication provider datasets, the Canadian Radio-television and Telecommunication Commission (CRTC) estimates that broadband services with advertised speeds that meet its basic universal service targets (50 Mbps download and 10 Mbps upload) are available to 84 percent of households in Canada. In rural areas however, services that meet CRTC’s speed targets are available to 37 percent of households.Moreover, effective speeds and service quality levels that suppliers deliver and users experience tend to fall well below the government’s aspirational targets. In response to demand for better broadband, a variety of initiatives are directing public investment to the deployment of regional and rural broadband networks, which are typically owned and operated by private companies. There remains a serious lack of relevant data and its effective use in rural broadband strategies and project management. Evidence from the literature suggest that this affects the degree and quality of geo-spatial and econometric analysis resulting in a limited empirical basis to allocate scarce public investments, engage consumers/communities, and assess the outcomes of rural broadband initiatives ex post. While investments in rural connectivity have vastly increased in recent years in Canada, this paper questions if the body of knowledge to inform these initiatives has grown sufficiently to ensure their effectiveness and sustainability. With examples from southern Ontario, Canada, we examine the relevant literature, characterize the broadband data challenge and discuss the importance of proprietary provider data cross-referenced with user experience data.

Keywords: broadband, rural, telecommunications, economic, mapping, Canada, Ontario

JEL Classification: A1, L96, L86, H1, H4, H5, 013, 033, 038, 04, 051, Q16, Q18, L13, L32, L38