A country's mix of products predicts its subsequent pattern of diversification and economic growth. But does this product mix also predict income inequality? Here we combine methods from econometrics, network science, and economic complexity to show that countries exporting complex products-as measured by the Economic Complexity Index-have lower levels of income inequality than countries exporting simpler products. Using multivariate regression analysis, we show that economic complexity is a significant and negative predictor of income inequality and that this relationship is robust to controlling for aggregate measures of income, institutions, export concentration, and human capital. Moreover, we introduce a measure that associates a product to a level of income inequality equal to the average GINI of the countries exporting that product (weighted by the share the product represents in that country's export basket). We use this measure together with the network of rela ted products-or product space-to illustrate how the development of new products is associated with changes in income inequality. These findings show that economic complexity captures information about an economy's level of development that is relevant to the ways an economy generates and distributes its income. Moreover, these findings suggest that a country's productive structure may limit its range of income inequality. Finally, we make our results available through an online resource that allows for its users to visualize the structural transformation of over 150 countries and their associated changes in income inequality during 1963-2008

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them.We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps.

The package diverse provides an easy-to-use interface to calculate and visualize different aspects of diversity in complex systems. In recent years, an increasing number of research projects in social and interdisciplinary sciences, including fields like innovation studies, scientometrics, economics, and network science have emphasized the role of diversification and sophistication of socioeconomic systems. However, so far no dedicated package exists that covers the needs of these emerging fields and interdisciplinary teams. Most packages about diversity tend to be created according to the demands and terminology of particular areas of natural and biological sciences. The package diverse uses interdisciplinary concepts of diversity-like variety, disparity and balance-as well as ubiquity and revealed comparative advantages, that are relevant to many fields of science, but are in particular useful for interdisciplinary research on diversity in socioeconomic systems. The package diverse provides a toolkit for social scientists, interdisciplinary researcher, and beginners in ecology to (i) import data, (ii) calculate different data transformations and normalization like revealed comparative advantages, (iii) calculate different diversity measures, and (iv) connect diverse to other specialized R packages on similarity measures, data visualization techniques, and statistical significance tests. The comprehensiveness of the package, from matrix import and transformations options, over similarity and diversity measures, to data visualization methods, makes it a useful package to explore different dimensions of diversity in complex systems.In recent years, an increasingly large number of research projects in social and interdisciplinary sciences are exploring the role of diversity in complex socioeconomic systems. These new approaches in social and interdisciplinary sciences use existing diversity concepts from biological and natural sciences, but also have their own particular needs and concepts. For instance, recent work in economics, scientometrics and network science has highlighted the importance of diversification processes in complex systems, such as research, financial and energy portfolios, cultural diversity, the diversity of ties in social and economic networks, or the emergence of new or related scientific and economic fields (Hidalgo et al., 2007;Frenken et al., 2007;Rafols et al., 2010;Chavarro et al., 2014;Guevara et al., 2016;Eagle et al., 2010;Farchy and Ranaivoson, 2011).Here we present the package diverse which aims to provide a useful toolkit for social scientists and interdisciplinary teams to measure and visualize diversity in socioeconomic systems, by providing

Research on economic diversification and complexity has made significant advances in understanding economic development processes, but has only recently explored environmental and social sustainability considerations. In this article we evaluate the current state of this emerging literature and reveal 13 research gaps. A total of 35 different keywords and methods from structured literature reviews and network science helped to identify 374 scientific articles between 1988 and 2020 and revealed a fragmented research landscape around three larger network communities: (1) industrial policies, climate change, and green growth; (2) economic complexity and its association with inequality and environmental sustainability; and (3) economic diversification, including studies on livelihood diversification in poor areas. Economic complexity research applies new empirical methods and considers both social and environmental sustainability, but seldom scrutinizes theory and policy. Industrial policy research focuses on green growth policies but tends to omit social sustainability issues and advanced empirical methods. Research on economic diversification in poor regions provides insights on the livelihood diversification of farmers, but is disconnected from the economic complexity and industrial policy research. This review helps to summarize the main contributions and shows pathways for potential mutual learning between these communities for the sake of sustainable development.

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This book combines the human development approach with innovation economics to explore the effects that structural economic change has on human development. While economic diversification can provide valuable new social choices and capabilities, it also tends to lead to more complex decision processes and changes to the set of capabilities required by people to self-determine their future. Within this process of structural transformation, social networks are crucial for accessing information and social support, but networks can also be a root cause of exclusion and inequality reproduction. This implies the need to encourage innovation and economic diversification beyond production expansion, focusing on the promotion of human agency and social inclusion. This book provides such a modern perspective on development economics, emphasizing the role of social networks, economic diversity and entrepreneurship for social welfare. The author discusses how innovation, social networks, economic dynamics and human development are interlinked, and provides several practical examples of social and micro-entrepreneurship in contexts as diverse as Peruvian rural villages and Brazil's urban areas. The interdisciplinary perspective put forward in this book illustrates theoretical and methodological methods of exploring the complexity of development in a practical and relevant way. It also provides useful information about structural factors which need to be considered by practitioners when designing pro-poor growth policies. Furthermore, the coverage of the core concepts of innovation, networks and development economics, enriched with multiple examples, makes it a valuable resource for scholars and advanced students of modern development economics.

Development studies on the middle-income trap have highlighted the challenges for developing economies to transform their productive systems from simple towards high valueadded activities. Here, we use trade data of 116 countries to quantify the stages of productive sophistication and reveal the critical phase that countries encounter at intermediate levels of economic sophistication. Our results reveal that only five countries (i.e. Ireland, Israel, Hungary, Singapore, and South Korea) overcame the gravitation towards simple products and fully transformed their economies towards complex products between 1970 and 2010. They successfully made use of windows of opportunities in the digital and electronics sectors through smart industrial policies that promoted endogenous skills and access to international knowledge sources. In contrast, countries like Brazil or South Africa still struggle with the gravitation towards simple economic activities, social fragmentation, and a lack of coherent industrial policies.