The new paradigm of economic complexity.

Economic complexity offers a potentially powerful paradigm to understand key societal issues and challenges of our time. The underlying idea is that growth, development, technological change, income inequality, spatial disparities, and resilience are the visible outcomes of hidden systemic interactions. The study of economic complexity seeks to understand the structure of these interactions and how they shape various socioeconomic processes. This emerging field relies heavily on big data and machine learning techniques. This brief introduction to economic complexity has three aims. The first is to summarize key theoretical foundations and principles of economic complexity. The second is to briefly review the tools and metrics developed in the economic complexity literature that exploit information encoded in the structure of the economy to find new empirical patterns. The final aim is to highlight the insights from economic complexity to improve prediction and political decision-making. Institutions including the World Bank, the European Commission, the World Economic Forum, the OECD, and a range of national and regional organizations have begun to embrace the principles of economic complexity and its analytical framework. We discuss policy implications of this field, in particular the usefulness of building recommendation systems for major public investment decisions in a complex world.

Using structural diversity to measure the complexity of technologies.

The paper introduces structural diversity as a new approach to quantify the complexity of technologies. By modeling technologies as combinatorial networks, a measure of technological complexity is derived that represents the diversity of (sub-)network topologies in these networks. It is further argued that this measure can be empirically approximated with the Network Diversity Score (NDS). The paper also presents an application of this approach to European patent data from 1980 to 2015. On this basis, the measure of structural diversity is shown to replicate a number of stylized facts commonly associated with technological complexity: Complexity increases over time and younger technologies are more complex than older technologies. Complex technologies are also associated to larger R&D efforts and require more collaborative R&D activities. Lastly, when controlling for technologies' size, technologies scoring high on structural diversity are also shown to concentrate in space.

Analyzing demand-side efficiency in global health: an application to maternal care in Vietnam.

This article investigates demand-side efficiency in global health-or the efficiency with which health system users convert public health resources into health outcomes. We introduce and explain the concept of demand-side efficiency as well as quantitative methods to empirically estimate it. Using a robust nonparametric form of technical efficiency analysis, we estimate demand side efficiency and its social determinants. We pilot these methods looking at how efficiently pregnant women in Northern Vietnam convert public health resources into appropriate maternal care as defined by national policy. We find that women who live in non-mountainous geographies, who are formally employed, who are pregnant with a boy and who are ethnic minorities are all more likely to be efficient at achieving appropriate care. We find no significant association between wealth or education and efficiency. Our results suggest that, in the Vietnamese context, women who are the most likely to achieve appropriate maternal care, are not necessarily the most likely to do so efficiently. Women who live in non-mountainous geographies and who are formally employed are both more likely to achieve appropriate care and to do so efficiently. Yet ethnic minority women, who do not systematically achieve better care, are more likely to be efficient or to achieve better care when compared with those with the same endowment of public health resources. On the methodological level, the pilot highlights that this approach can provide useful information for policy by identifying which groups of people are more and less likely to be efficient. By understanding which groups are more likely to be efficient-and in turn how and why-it may be possible to devise policies to promote the drivers of, or conversely address the constraints to, optimizing demand-side efficiency.