Currently we may have access to large databases, sometimes coined as Big Data, and for those large datasets simple econometric models will not do. When you have a million people in your database, such as insurance firms or telephone providers or charities, and you have collected information on these individuals for many years, you simply cannot summarize these data using a small-sized econometric model with just a few regressors. In this chapter we address diverse options for how to handle Big Data. We kick off with a discussion about what Big Data is and why it is special. Next, we discuss a few options such as selective sampling, aggregation, nonlinear models, and variable reduction. Methods such as ridge regression, lasso, elastic net, and artificial neural networks are also addressed; these latter concepts are nowadays described as so-called machine learning methods. We see that with these methods the number of choices rapidly increases, and that reproducibility can reduce. The analysis of Big Data therefore comes at a cost of more analysis and of more choices to make and to report.

The global pandemic of COVID-19 that began in late 2019 highlighted the importance of rapid and thorough investigations of outbreaks. The response to COVID-19 was at a scale not previously seen, involving all sectors of society, including government and private industry. To control and minimise the impact of COVID-19, huge and costly efforts were required to effectively coordinate many different organisations, many of which were not primarily concerned with public health. This type of re-focusing of resources is common in outbreak and public health emergency settings, but is rarely seen at such scale. In this chapter we look at outbreak investigation in more detail and, in doing so, focus on infectious diseases, although not exclusively, because other agents such as toxins and chemicals can also result in ‘outbreaks’ of non-communicable intoxications, injuries and cancer.

This chapter provides an overview of Slavic syllable structure from both synchronic and diachronic perspectives. It begins by introducing some general phonological background on the syllable and reviewing key concepts, such as syllabification and sonority. The discussion further focuses on specific aspects of Slavic syllable structure: inventories of syllable-initial and syllable-final consonants and consonant clusters, and the composition of syllable peaks (vowels and syllabic consonants). This is done by situating patterns characteristic of Slavic in the context of cross-linguistic typology of syllable structure, as well as by highlighting common historical sources and more recently developed differences among individual Slavic languages. The chapter concludes with a short review of recent experimental and corpus work on Slavic syllable structure.

The chapter sums up the discussion in the previous three chapters, addressing the question of where we are now in the transition process between CAPE and modernity. Was the transition short, as the material side of the story might suggest, or longer, as the social side of the story suggests? Are we now in modernity proper, or are we still in the transition? The chapter proceeds by looking for significant clusters of events, both material and social, that can give guidance as to how these questions might be answered. It argues that the unfolding contradiction between unrestrained development and the limits of planetary carrying capacity is emerging as the key dialectic for the future of humankind.

Knut Heim examines the literary and historical contexts of wisdom literature, taking the book of Proverbs as a case study, and surveying the work of key scholars in the field. Beginning with literary context, he argues that the sayings are organised into ‘clusters’ through linguistic and thematic links with their neighbours, and that this context has hermeneutical significance. Particularly important is the placement of religious proverbs, which are well integrated with their surroundings. This calls into question the scholarly assumption that religious elements are a late addition to the book, and that wisdom was originally a ‘secular’ endeavour. Rather, elements like the ‘fear of the Lord’ were already embedded within the sayings collections by the time an editor added chapters 1–9. This has implications for the historical development of Proverbs and, more broadly, of wisdom in Israel.

In this chapter, we review the literature on interdependent routines. We group previous studies on routine interdependence around key concepts – boundaries & intersections, clusters, ecologies, and bundles – and highlight the different analytical foci and results of each group. Hence, we make an argument for leveraging the analytical differences of such concepts as cluster and ecologies, rather than treating them as synonyms. In closing, we point out several avenues for future research.

The second chapter concentrates on the concept of entrepreneurial ecosystems, providing various ways to conceptualize and define it and then moving on to discuss its importance for supporting economic development. Given the growing body of work on entrepreneurial ecosystems, the chapter first outlines how the field of entrepreneurial ecosystems evolved from existing work on clusters and carries much of its assumptions around homogeneity of actors. In contrast to these assumptions, we demonstrate that actors are not homogenous but heterogeneous and that existing concepts of entrepreneurial ecosystems do not differentiate among entrepreneurs as actors within ecosystems. These arguments are further elaborated on with evidence in the chapters that follow.

Interactions between firms, universities and other actors in the innovation system are essential for open innovation. Empirical literature on the relationship between open innovation strategies and firm performance in low-income countries however is less and heavily biased towards developed countries. This chapter examines the role of open innovation strategies on the innovation performance of firms in Ghana and Tanzania, and explores further the roles of university–industry collaborations and the participation of regional and global production networks on capability development of African firms. Based on primary data, our results show that open innovation strategies – external knowledge search, clusters, collaboration and regional value chains – help firms in Ghana and Tanzania to circumvent the many constraints they face in their day-to-day operations. In terms of policy, the chapter suggests that there is a need for policy actions that directly help to support and promote open innovation strategies within and between formal and informal sectors

In this chapter, we analyse the rationale of regional policies, discuss the evidence, and highlight some methodological problems. In doing so, we also take a closer look at policy consequences that follow from or can be linked to the models discussed throughout the book. In some of our thought-experiments, we take the models of the book literally and ask, what would be the lessons for policy makers? When we discuss actual cluster/regional policy thinking, but also when we discuss hypothetical policies by sticking as closely as possible to our models, there are always three main elements to take into account. First, whether it is a people- or place-based policy (or a mix of these two). Second, to go beyond the observation that a policy can change spatial economic outcomes by showing that a policy is somehow welfare improving. Third, to take into consideration the important macro/micro distinction in terms of regional policy effectiveness: a ‘good’ regional policy at the city or regional level does not necessarily imply that it is also beneficial for the economy as whole. The main message of the chapter is that regional policies should be handled with care.

In this chapter, we analyse the rationale of regional policies, discuss the evidence, and highlight some methodological problems. In doing so, we also take a closer look at policy consequences that follow from or can be linked to the models discussed throughout the book. In some of our thought-experiments, we take the models of the book literally and ask, what would be the lessons for policy makers? When we discuss actual cluster/regional policy thinking, but also when we discuss hypothetical policies by sticking as closely as possible to our models, there are always three main elements to take into account. First, whether it is a people- or place-based policy (or a mix of these two). Second, to go beyond the observation that a policy can change spatial economic outcomes by showing that a policy is somehow welfare improving. Third, to take into consideration the important macro/micro distinction in terms of regional policy effectiveness: a ‘good’ regional policy at the city or regional level does not necessarily imply that it is also beneficial for the economy as whole. The main message of the chapter is that regional policies should be handled with care.