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Roberta Capello and Peter Nijkamp

The space-economy has never been static, but has always shown a state of flux. Regions are normally in transition; they are work in progress. As a consequence, we observe a complex evolution of regional systems that varies between growth and decline. Static location and allocation theories may be helpful in understanding underlying structures in regional economies, but do not offer a full-scale picture of the development of multi-actor processes and of the perpetual or temporal impediments for regional growth and prosperity. The conceptualization and solid explanation of regional growth, and differences therein, is still largely a mystery for the research community in many countries. There is no uniform panacea for enhancing or accelerating the development trajectory of regions in a national or supranational economy. Therefore, regional policy is still in many cases a black box; the outcomes of intensified regional growth strategies are often largely unpredictable. Best guesses are more common than testable and operational estimates of policy impacts. Against the above-mentioned backgrounds, the editors of the Handbook of Regional Growth and Development Theories published a decade ago a comprehensive volume with a rich collection of advanced contributions on the above challenges in regional economics and regional science. In the ten years since then the world, both the empirical regional world and the theoretical and empirical reflection on growth and development issues, has not come to a standstill. We have become sadder and wiser after economic crises, regional fragmentation trends, the introduction of radical technological innovation, and the awareness of failures of regional policy. However, we have also enriched our knowledge horizon, with new insights and new methods and theories of regional analysis. The time has now come to take a refreshing and new look at the achievements of regional growth and development theories.

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Martin Jones

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Edited by John R. Bryson, Lauren Andres and Rachel Mulhall

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Lasse Gerrits and Stefan Verweij

We explain and demonstrate how the selected cases have to be prepared for the actual comparison. This involves a serious effort with regard to the interpretation of the case materials. In QCA, this process of interpreting data is guided by calibration, where raw (qualitative) case data are transformed into quantitative values. Calibration is important because it systematizes interpretation and makes it transparent. There are three principle types of calibration in QCA: crisp-set QCA, fuzzy-set QCA, and multi-value QCA. We explain and demonstrate the different types of calibration using real examples. We also discuss good practices that will help the researcher in making sound decisions when calibrating. The calibration results in a calibrated data matrix, which forms the input for the formal comparison in QCA. Having completed this chapter, the researcher will be able to start the comparison.

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Lasse Gerrits and Stefan Verweij

We explain why it is important to research specific cases and how exactly cases are to be understood and studied using QCA. Cases allow the researcher to account for the heterogeneity, uniqueness, and contextuality of projects. Whereas the term ‘case’ is often used indiscriminately, in QCA it is a clearly defined and important building block. In QCA, cases are conceptualized as configurations of conditions. This configurational nature highlights the complexity of the case. Cases can be researched in two principal ways: case-driven and theory-driven. The case-driven route is decidedly grounded in empirical material, with the boundaries and aspects of cases being constructed during the empirical research process. In the more theory-driven route, the boundaries and aspects of cases are defined by prior theories. Both routes constitute dialogues between data and theory. The chapter explains the concrete research steps involved in both routes.

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Lasse Gerrits and Stefan Verweij

We explain and demonstrate how the researcher can identify recurring patterns across cases on the basis of the calibrated data matrix, in a systematic and transparent way. The comparative process in QCA consists of three main steps. First, the calibrated data matrix needs to be transformed into a truth table. In the truth table, the cases are sorted across the logically possible configurations of conditions. Second, the truth table has to be minimized. This is done through the pairwise comparison of truth table rows that are considered to agree on the outcome and differ in their score in but one of the conditions. The result of the minimization is a solution formula. Third, the solution formula needs to be interpreted. Two common issues in the truth table minimization are limited diversity and logical contradictions. We present various strategies for dealing with these issues.

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Lasse Gerrits and Stefan Verweij

In this concluding chapter, some of the main issues concerning the evaluation of complex infrastructure projects with QCA are revisited. First, QCA’s capacity to truly capture and study the complexity of the development of infrastructure projects is discussed. QCA’s take on complex causality is relatively static because it does not explicitly integrate the time dimension. Various strategies to integrate time in QCA are discussed, including Temporal QCA (TQCA) and Time-Series QCA (TS/QCA). The different strategies have their strengths and weaknesses and they relate to different research steps (i.e., the case, the calibration, and the comparison) involved in QCA. Second, the deployment of QCA in real-world evaluations and various issues evaluators may run into are discussed. These issues include learning and political accountability, the presentation and visualization of results, and the transfer of lessons learned.

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Lasse Gerrits and Stefan Verweij

We argue that infrastructure projects are complex and that evaluations of such projects need to do justice to that complexity. The three principal aspects discussed here are heterogeneity, uniqueness, and context. Evaluations that are serious about incorporating the complexity of projects need to address these aspects. Often, evaluations rely on single case studies. Such studies are useful because they allow researchers to focus on the heterogeneous, unique, and contextual nature of projects. However, their relevance for explaining other (future) projects is limited. Larger-n studies allow for the comparison of cases, but they come with the important downside that their relevance for explaining single projects is limited because they cannot incorporate heterogeneity, uniqueness, and context sufficiently. The method Qualitative Comparative Analysis (QCA) presents a promising solution to this conundrum. This book offers a guide to using QCA when evaluating infrastructure projects.

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Lasse Gerrits and Stefan Verweij

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Åke E. Andersson and David Emanuel Andersson

In this chapter knowledge capital is seen as a specific combination of subsets of human and social capital, much as real estate capital combines physical and social capital. Knowledge capital is a key factor that drives economic growth and development. Knowledge is different from information; it is more complex and multifaceted, as it can be private or public. It can be embodied in machinery or tacit knowledge in humans, but dissemination processes cause its disembodiment. Scientific knowledge has become an increasingly important precondition for the emergence of investments in industrial research and development. The broad spectrum of new technologies in the pharmaceutical, biotechnological, information and transportation industries would have been unthinkable without earlier fundamental creativity in mathematics, physics, chemistry and biology. Scientific breakthroughs almost always occur many decades before being exploited by entrepreneurial innovators. Rogers Hollingsworth has shown that the increasing complexity of many products and production systems requires a reorganization of scientific research with a greater emphasis on multidisciplinary departments and laboratories. The possibility of exploiting advantages of a diversified scientific knowledge base also points toward increasing dynamic comparative advantages of locating universities and research institutes in large cities. Quantitative analyses of science networks show that the San Francisco Bay Area, Boston, London, Tokyo, Paris and Randstad (Amsterdam) are the most important nodes in the world of science, with Beijing, Seoul and Shanghai exhibiting the highest growth rates in science output among large cities. The advantages of dynamic interactions between scientific creativity and industrial development will reinforce the long-term sustainable growth in regions that host large-scale agglomerations of scientific research.