The COVID-19 pandemic forced many higher education institutions (HEIs) across the world to cancel face-to-face teaching, close campus facilities, and displace staff and students to work and learn from home. Given the persistent nature of the pandemic, many HEIs have continued to deliver courses online and/or use a blended learning approach. However, there are concerns around differences in student access to digital learning resources while at home, including high quality broadband connectivity. This is important, since variation in connectivity may impact the type of online/blended model that faculty can deliver or constrain student engagement with online content. In this context, this paper combines national data on the domiciles of students enrolled in Irish HEIs with detailed spatial data on broadband coverage to estimate the number of higher education students ‘at risk’ of poor access to high quality internet connectivity. Overall it finds that one-in-six students come from areas with poor broadband coverage, with large disparities by geography and by HEI. It also finds that students from the poorest broadband coverage areas are more likely to be socioeconomically disadvantaged. As a result, this paper recommends that HEIs use their detailed registration data to help identify and support at-risk students. In particular, the results suggest that some HEIs may need to prioritise access to campus facilities and services to less well-off students living in poor broadband coverage areas.

This article examines variation in the income elasticity of household energy demand across the energy expenditure distribution using expenditure data from the five most recent Household Budget Surveys (HBSs) in Ireland: the 1987, 1994/1995, 1999/2000, 2004/2005 and 2009/2010 HBS. The analysis uses a two-stage instrumental variable quantile regression approach and is based on each HBS cross section, as well as the overall pooled observations. The estimated elasticities are compared across low- and high-energy-consumption scenarios and to a benchmark elasticity estimated using two-stage least squares. The results provide evidence that there is significant variation in the income elasticities across the energy expenditure distribution and that care must be taken when using the constant mean elasticity for policy purposes. More specifically, any examination of the future impact of a change in income support policy measures on energy consumption should recognize the substantial context-dependent variation in the income elasticity.

There is increasing interest among policymakers in small open economies in the use of star-scientist recruitment policies to catalyse the development of local clusters in targeted research areas. We use Scopus to assemble a dataset on over 1.4 million publications and subsequent citations for Denmark, Ireland and New Zealand from 1990 to 2017. An event-study model is used to estimate the dynamic effects of a star arrival on quality-adjusted research output at both the department and matched individual incumbent levels. Star arrivals are associated with statistically significant increases in department output (excluding the output of the star) of between 12% and 25% after 4 years. At the incumbent level, star arrivals lead to an approximately 5% increase in individual output, with substantially larger increases for incumbents who co-author with the star.