Geographic information has been a corner stone of the modern state since its inception. When cartographers and geographers
measured and categorized forests to allow for an efficient taxation of real estate, the modern tax state was born. Scientific forestry
was only the first step in this manifestation of the tax state, followed by similar approaches for agriculture, land management, and
urban and rural planning (Scott, 1998). Indeed, the systematic collection of information about the geophysical structure,
population, business activities, and other aspects of socio-economic life in territories belongs to the core activities of public agents.
It is often said, yet nowhere empirically proven, that >80% of government-held data have a spatial dimension (Garson and Biggs,
1992). Regardless of whether this figure is precise or not, we arguably live in an era where spatially based data are becoming Au1
increasingly important for everyday life and public policy by informing the search process for effective and efficient policies. Given
this, it is perhaps unsurprising that local governments have been at the forefront of generating geodata (Hissong and Couret, 1999;
Ventura, 1995).
Geographical information has also started playing a more important role in our understanding of the economy and innovation:
the fact that industries cluster geographically has led to an appreciation of economic geography and the importance of spatial data
for trade models and innovation economics (Asheim et al., 2011; Foray, 2015; Fujita et al., 2001). In this article, we examine the
role of geographic information in the formulation of policies to support local economic development and innovation. We describe how spatial concentration of economic and innovative activity has persisted in the face of improvements in information and communication technologies (ICT) that, some believed, would result in the “death of distance” (Cairncross, 1997). The same technologies
that were supposed to help businesses to communicate and collaborate regardless of their location have also sped up the pace of
innovation, which in turn contributes to geographical concentration (Glaeser, 2011). In the last 30 years, a panoply of theories and
frameworks have emerged to explain this feature of economic life, with concepts such as industrial districts, industrial clusters,
regional systems of innovation, entrepreneurial ecosystems, and creative cities, among others (Becattini, 2004; Cooke et al., 1997;
Florida, 2012; Porter, 2013).
After reviewing these theories, we discuss their implications, in terms of the geographical level at which policies are best
designed and implemented, and regarding the policy instruments to be adopted. The implications are manifested in policy trends
such as devolution of decision-making from central to local government in the United Kingdom (Heseltine, 2012), and in the idea
of the “metropolitan revolution” that embraces cities—and city halls—as key players in innovation and economic policy in the
United States (Katz and Bradley, 2013). Unfortunately, many spatially focused economic and innovation policies have not
performed as expected. This is at least in part a consequence of their lack of attention to local conditions and history. Two
influential policy frameworks have emerged in response to this: smart specialization (Foray, 2015) and entrepreneurial discovery
(Hausmann and Rodrik, 2003). These policies presume that a vision for the future is not enough to create sustainable local economic growth, even if it is accompanied by large public investments. Robust geographic information about the state of the local economy is necessary to identify realistic and promising avenues of development, as well as the barriers that need to be overcome and the local actors
that need to be engaged. (In the absence of this information, and of the possibility of targeting local economic development policies effectively, some argue that it is preferable to avoid proactive industrial and economic policies at all. Instead, policymakers
ought to focus on framework conditions such as investments in infrastructure, education, and skills, which are agnostic
with regard to different sectors (Nathan and Overman, 2013).)1 In section “Geographic Information for Innovation Policy”,
we describe different data sources and methods for obtaining such information. We discuss the limitations of traditional
approaches relying on official data based on standard industrial taxonomies, as well as indicators of scientific, technological,
and innovation (STI) outputs such as academic publications or patents. We highlight the potential of new web and open data
sources and analytical methods that provide a more granular, precise, timely, and relational view of local economies and
innovation systems.
How this geographic information can be incorporated into policymaking processes is an activity that is not devoid of challenges. In particular, the increasing availability of information gives rise to the twin risks of (1) overburdening policymaker attention leading to decision paralysis and (2) multiplying the number of issues that policymakers believe must be addressed—and, therefore, the number of policy interventions, at the risk of rising inefficiency, complexity, and potential distortions (Baumgartner and Jones, 2015).
KLEIBRINK Alexander;
MATEOS Juan;
2017-09-20
Elsevier
JRC106596
9780128046609,
http://www.sciencedirect.com/science/article/pii/B9780124095489096743,
https://publications.jrc.ec.europa.eu/repository/handle/JRC106596,
10.1016/B978-0-12-409548-9.09674-3,
