Karyn Morrissey (p)

Emmanouil Tranos

Vast quantities of data have always been collected however the last decade has seen new methods of collecting this data emerging. Web based surveys are one promising avenue for researchers to collect data.

In June 2011 the BBC Lab UK carried out a web-based survey on the causes of mental distress. The ‘Stress Test’ was launched on ‘All in the Mind’ a BBC Radio 4 programme and the test’s URL was publicised on radio and TV broadcasts, and made available via BBC web pages and social media. Given the large amount of data created, over 32,800 participants, with corresponding diagnosis, demographic and socioeconomic characteristics; the dataset are potentially an important source of data for population based research on depression and anxiety.

However, as respondents self-selected to participate in the online survey the survey may comprise a non-random sample. It may be only individuals that listen to BBC Radio 4 and/or use their website that participated in the survey. In this instance using the Stress Test data for wider population based research may create sample selection bias. Focusing on the depression component of the Stress Test, this paper presents an easy-to-use method, the Two Step Probit Selection Model, to detect and statistically correct selection bias in the Stress Test.

Using a Two Step Probit Selection Model; this paper did not find a statistically significant selection on unobserved factors for participants of the Stress Test. That is, survey participants who accessed and completed an online survey are not systematically different from non-participants on the variables of substantive interest.

Concluding comments are offered on these findings with regard to the use of Big Data in Regional Science.

Emmanouil Tranos (p), Tasos Kitsos , Raquel Ortega Argiles

Karyn Morrissey

This paper maps the early adoption patterns of digital economic activities in the UK regions and tests whether such early adoption patterns have affected future economic development and entrepreneurship trajectories. Obtaining an understanding of such effects can provide valuable inputs to digital economy strategies as it can justify demand-side interventions.

In order to do so, we use unique data from the Internet Archive, the most complete archive of web pages (Holzmann et al., 2016; Ainsworth et al., 2011). Specifically, we employ the JISC UK Web Domain Dataset, which is a subset of the Internet Archive and contains billions of URLs of webpages within the .uk domain. These webpages have been archived by the Internet Archive during the period 1996-2013 and the data also include the archiving timestamp. The British Library, which curates these data, has generated a subset of circa 2.5 billion URLs of archived webpages which contain at least one UK postcode. We use these data in order to create proxies of the aggregated, online, commercial activities which take place within UK regions. While the spatial dimension (postcode) of these data enables us to study the spatiality of the online commercial activities, the longitudinal dimension (archiving timestamp) of the data enables us to identify regions with early adopters of such technologies.

These proxies will be then utilised in an econometric framework in order to test their effect on the level of economic development and on regional business population. We are particularly interested in the effect of concentration of early adopters and therefore we will utilise these proxies in a lagged form. Our hypothesis is that the concentration of early adopters of online technologies for commercial purposes has positively affected the current growth and specialisation patterns of firms within UK regions. The latter builds upon an evolutionary economic geography framework, according to which the distribution of economic activities in space is the outcome of path-dependent historical processes (Dosi and Nelson, 2010). In addition, our research framework investigates the capacity of regions to move towards activities of higher value added (Boschma and Frenken, 2006; Boschma and Martin, 2007; Frenken et al., 2007; Boschma, Mirondo and Navarro, 2012) and, therefore, move towards smart specialisation (Foray, 2014).