What can the millions of random treatments in nonexperimental data reveal about causes?

We propose a new method to estimate causal effects from nonexperimental data. Each pair of sample units is first associated with a stochastic 'treatment' - differences in factors between units - and an effect - a resultant outcome difference. It is then proposed that all such pairs can be combined to provide more accurate estimates of causal effects in observational data, provided a statistical model connecting combinatorial properties of treatments to the accuracy and unbiasedness of their effects. The article introduces one such model and a Bayesian approach to combine the $O(n^2)$ pairwise observations typically available in nonexperimnetal data. This also leads to an interpretation of nonexperimental datasets as incomplete, or noisy, versions of ideal factorial experimental designs. This approach to causal effect estimation has several advantages: (1) it expands the number of observations, converting thousands of individuals into millions of observational treatments; (2) starting with treatments closest to the experimental ideal, it identifies noncausal variables that can be ignored in the future, making estimation easier in each subsequent iteration while departing minimally from experiment-like conditions; (3) it recovers individual causal effects in heterogeneous populations. We evaluate the method in simulations and the National Supported Work (NSW) program, an intensively studied program whose effects are known from randomized field experiments. We demonstrate that the proposed approach recovers causal effects in common NSW samples, as well as in arbitrary subpopulations and an order-of-magnitude larger supersample with the entire national program data, outperforming Statistical, Econometrics and Machine Learning estimators in all cases..

The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems

In economic systems, the mix of products that countries make or export has been shown to be a strong leading indicator of economic growth. Hence, methods to characterize and predict the structure of the network connecting countries to the products that they export are relevant for understanding the dynamics of economic development. Here we study the presence and absence of industries at the global and national levels and show that these networks are significantly nested. This means that the less filled rows and columns of these networks' adjacency matrices tend to be subsets of the fuller rows and columns. Moreover, we show that nestedness remains relatively stable as the matrices become more filled over time and that this occurs because of a bias for industries that deviate from the networks' nestedness to disappear, and a bias for the missing industries that reduce nestedness to appear. This makes the appearance and disappearance of individual industries in each location predictable. We interpret the high level of nestedness observed in these networks in the context of the neutral model of development introduced by Hidalgo and Hausmann (2009). We show that, for the observed fills, the model can reproduce the high level of nestedness observed in these networks only when we assume a high level of heterogeneity in the distribution of capabilities available in countries and required by products. In the context of the neutral model, this implies that the high level of nestedness observed in these economic networks emerges as a combination of both, the complementarity of inputs and heterogeneity in the number of capabilities available in countries and required by products. The stability of nestedness in industrial ecosystems, and the predictability implied by it, demonstrates the importance of the study of network properties in the evolution of economic networks.Comment: 26 page

The struggle for existence in the world market ecosystem

The global trade system can be viewed as a dynamic ecosystem in which exporters struggle for resources: the markets in which they export. We can think that the aim of an exporter is to gain the entirety of a market share (say, car imports from the United States). This is similar to the objective of an organism in its attempt to monopolize a given subset of resources in an ecosystem. In this paper, we adopt a multilayer network approach to describe this struggle. We use longitudinal, multiplex data on trade relations, spanning several decades. We connect two countries with a directed link if the source country's appearance in a market correlates with the target country's disappearing, where a market is defined as a country-product combination in a given decade. Each market is a layer in the network. We show that, by analyzing the countries' network roles in each layer, we are able to classify them as out-competing, transitioning or displaced. This classification is a meaningful one: when testing the future export patterns of these countries, we show that out-competing countries have distinctly stronger growth rates than the other two classes

Unveiling relationships between crime and property in England and Wales via density scale-adjusted metrics and network tools

Scale-adjusted metrics (SAMs) are a significant achievement of the urban scaling hypothesis. SAMs remove the inherent biases of per capita measures computed in the absence of isometric allometries. However, this approach is limited to urban areas, while a large portion of the world’s population still lives outside cities and rural areas dominate land use worldwide. Here, we extend the concept of SAMs to population density scale-adjusted metrics (DSAMs) to reveal relationships among different types of crime and property metrics. Our approach allows all human environments to be considered, avoids problems in the definition of urban areas, and accounts for the heterogeneity of population distributions within urban regions. By combining DSAMs, cross-correlation, and complex network analysis, we find that crime and property types have intricate and hierarchically organized relationships leading to some striking conclusions. Drugs and burglary had uncorrelated DSAMs and, to the extent property transaction values are indicators of affluence, twelve out of fourteen crime metrics showed no evidence of specifically targeting affluence. Burglary and robbery were the most connected in our network analysis and the modular structures suggest an alternative to "zero-tolerance" policies by unveiling the crime and/or property types most likely to affect each other

Modelling Hierarchy and Specialization of a System of Cities from an Evolutionary Perspective on Firms' Interactions

Despite their great diversity, most systems of cities show remarkably similar patterns when comparing the size distribution and the economic specialization of their constitutive cities. The universality of these patterns sparked the interest of geographers, economists and physicists. However, until now, no economic model has relied on a micro-based and evolutionary approach to reproduce these regularities. In this chapter, we intend to fill this gap by proposing a model where the micro dynamics of localized firms generate the two macro regularities of size distribution and economic specialization. The model is based on boundedly rational firms’ competition and path dependent innovation. We discuss the possible emergence of macro properties from these micro behaviors of firms

Policies for new path development: the case of Oxfordshire

This chapter reflects on how evolutionary economic geography (EEG) can be extended to incorporate public policy in its explanations of path development. A weakness of EEG is the poor conceptualisation of the role of the state (central, regional, local) in regional path development. It is therefore argued that a multi-scalar perspective of policy is required and that a large set of policies deserve attention. Oxfordshire in the UK is used to explore the link between public policy and path development

Location determinants of green technological entry: evidence from European regions

In this paper, we explore the spatial distribution and the location determinants of new green technology-based firms across European regions. Integrating insights from evolutionary economic geography and the literature on knowledge spillovers, we study the importance of new knowledge creation and the conditioning role played by regional technological relatedness in fostering combinatorial opportunities underlying the process of green technological entry. The analysis is based on a dataset covering over 900 NUTS3 regions for 15 European countries obtained merging economic data from ESPON-Eurostat and patent information from the PATSTAT-CRIOS database for the period 1996–2006. Our results show that the geographical distribution of green technological entry across European regions is not evenly distributed, offering evidence of spatial path dependence. In line with this, we find evidence of a significant role played by the characteristics of the regional innovation system. New green innovators are more likely to develop in regions defined by higher levels of technological activity underlying knowledge spillovers and more dynamism in technological entry. Moreover, our findings point to an inverted-U relationship between regional technological relatedness and green technological entry. Regions whose innovation activity is defined by cognitive proximity to environmental technologies support interactive learning and knowledge spillovers underlying entrepreneurship in this specific area. However, too much relatedness may cause technological lock-ins and reduce the set of combinatorial opportunities

Regional labour market mobility. A network analysis of inter-firm relatedness

Labour market rigidity is known to hamper the proper adjustment of an economy, thus making it less resilient to shocks. This paper investigates the characteristics and resilience of the regional labour flow network in Veneto, a region famous for its industrial districts and the expertise of its workforce. A unique database of inter-firm worker mobility is used and the made-in-Italy relatedness to other industries is quantified. Descriptive results suggest that permanent-contract workers are more mobile within-sector than fixed-term contractors. The latter are more mobile across sectors. A finer disaggregation of the made-in-Italy industries shows that textile, food and woodwork are highly related to leisure-retail, logistics-wholesale and agriculture. These results can orient policy-making in getting faster labour reallocation. Network analysis establishes a number of stylised facts about labour flow networks, in particular, a hierarchical organisation of flows and a preference for workers to move from low-connected to high-connected firms and vice-versa, i.e. disassortativity. Unlike previous research, this paper identifies clusters of a non-spatial nature, that is, based on the intensity of labour flows. Regression analysis shows that labour mobility, both in and out, is beneficial for firms. However, being located inside labour clusters negatively affects firm performance. Interestingly, when these clusters include MNEs, the firm benefits. These results combined suggest that variety of connections prevails over standardisation