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Research Spotlight

Architects and engineers sitting around a work tableThe Value of Complementary Coworkers


Skills and knowledge are crucial for finding good jobs and earning high incomes. This explains why, in spite of the increasing costs, most of us consider education as a valuable investment. However, new research at the Growth Lab - recently published in Science Advances - shows that just having valuable skills oneself is insufficient. What is as, or possibly even more, important as one’s own education is how our skills relate to the skills of the people we work with. Moreover, coworker complementarities help answer a number of old questions: Why can workers with the same education earn drastically different wages? Why do large cities pay such high wages? And why do workers earn higher wages in large establishments than in small establishments, even if they have similar skills?

Recent Publications

Schetter, U. & Tejada, O., 2019. On Globalization and the Concentration of Talent.Abstract
We analyze how globalization affects the allocation of talent across competing teams in large matching markets. Assuming a reduced form of globalization as a convex transformation of payoffs, we show that for every economy where positive assortative matching is an equilibrium without globalization, it is also an equilibrium with globalization. Moreover, for some economies positive assortative matching is an equilibrium with globalization but not without. The result that globalization promotes the concentration of talent holds under very minimal restrictions on how individual skills translate into team skills and on how team skills translate into competition outcomes. Our analysis covers many interesting special cases, including simple extensions of Rosen (1981) and Melitz (2003) with competing teams.
The Value of Complementary Coworkers
Neffke, F., 2019. The Value of Complementary Coworkers. Science Advances , 5 (12). Publisher's VersionAbstract

As individuals specialize in specific knowledge areas, a society’s know-how becomes distributed across different workers. To use this distributed know-how, workers must be coordinated into teams that, collectively, can cover a wide range of expertise. This paper studies the interdependencies among co-workers that result from this process in a population-wide dataset covering educational specializations of millions of workers and their co-workers in Sweden over a 10-year period. The analysis shows that the value of what a person knows depends on whom that person works with. Whereas having co-workers with qualifications similar to one’s own is costly, having co-workers with complementary qualifications is beneficial. This co-worker complementarity increases over a worker’s career and offers a unifying framework to explain seemingly disparate observations, answering questions such as “Why do returns to education differ so widely?” “Why do workers earn higher wages in large establishments?” “Why are wages so high in large cities?”

Frank Neffke discusses his findings in this Growth Lab Podcast.

Nedelkoska, L. & Neffke, F., 2019. Skill Mismatch and Skill Transferability: Review of Concepts and Measurements. Papers in Evolutionary Economic Geography , 19 (21). Publisher's VersionAbstract
The notion of skills plays an increasingly important role in a variety of research fields. Since the foundational work on human capital theory, economists have approached skills through the lens of education, training and work experience, whereas early work in evolutionary economics and management stressed the analogy between skills of individuals and the organizational routines of firms. We survey how the concept of skills has evolved into notions such as skills mismatch, skill transferability and skill distance or skill relatedness in labor economics, management, and evolutionary approaches to economics and economic geography. We find that these disciplines converged in embracing increasingly sophisticated approaches to measuring skills. Economists have expanded their approach from quantifying skills in terms of years of education to measuring them more directly, using skill tests, self-reported skills and job tasks, or skills and job tasks reported by occupational experts. Others have turned to administrative and other large-scale data sets to infer skill similarities and complementarities from the careers of sometimes millions of workers. Finally, a growing literature on team human capital and skill complementarities has started thinking of skills as features of collectives, instead of only of individuals. At the same time, scholars in corporate strategy have studied the micro-determinants of team formation. Combined, the developments in both strands of research may pave the way to an understanding of how individual-level skills connect to firm-level routines.
Schetter, U., 2019. A Structural Ranking of Economic Complexity.Abstract
We propose a structural alternative to the Economic Complexity Index (ECI, Hidalgo and Hausmann 2009; Hausmann et al. 2011) that ranks countries by their complexity. This ranking is tied to comparative advantages. Hence, it reveals information different from GDP per capita on the deep underlying economic capabilities of countries. Our analysis proceeds in three main steps: (i) We first consider a simplified trade model that is centered on the assumption that countries’ global exports are log-supermodular (Costinot, 2009a), and show that a variant of the ECI correctly ranks countries (and products) by their complexity. This model provides a general theoretical framework for ranking nodes of a weighted (bipartite) graph according to some under- lying unobservable characteristic. (ii) We then embed a structure of log-supermodular productivities into a multi-product Eaton and Kortum (2002)-model, and show how our main insights from the simplified trade model apply to this richer set-up. (iii) We finally implement our structural ranking of economic complexity. The derived ranking is robust and remarkably similar to the one based on the original ECI.
Popularity Spikes Hurt Future Chances For Viral Propagation of Protomemes
Coscia, M., 2018. Popularity Spikes Hurt Future Chances For Viral Propagation of Protomemes. Communications of the ACM , 61 (1) , pp. 70-77. Publisher's VersionAbstract
A meme is a concept introduced by Dawkins12 as an equivalent in cultural studies of a gene in biology. A meme is a cultural unit, perhaps a joke, musical tune, or behavior, that can replicate in people's minds, spreading from person to person. During the replication process, memes can mutate and compete with each other for attention, because people's consciousness has finite capacity. Meme viral spreading causes behavioral change, for the better, as when, say, the "ALS Bucket Challenge" meme caused a cascade of humanitarian donations,a and for the worse, as when researchers proved obesity7 and smoking8 are socially transmittable diseases. A better theory of meme spreading could help prevent an outbreak of bad behaviors and favor positive ones.
Coscia, M., 2018. Using arborescences to estimate hierarchicalness in directed complex networks. PLoS ONE , 13 (1). Publisher's VersionAbstract
Complex networks are a useful tool for the understanding of complex systems. One of the emerging properties of such systems is their tendency to form hierarchies: networks can be organized in levels, with nodes in each level exerting control on the ones beneath them. In this paper, we focus on the problem of estimating how hierarchical a directed network is. We propose a structural argument: a network has a strong top-down organization if we need to delete only few edges to reduce it to a perfect hierarchy—an arborescence. In an arborescence, all edges point away from the root and there are no horizontal connections, both characteristics we desire in our idealization of what a perfect hierarchy requires. We test our arborescence score in synthetic and real-world directed networks against the current state of the art in hierarchy detection: agony, flow hierarchy and global reaching centrality. These tests highlight that our arborescence score is intuitive and we can visualize it; it is able to better distinguish between networks with and without a hierarchical structure; it agrees the most with the literature about the hierarchy of well-studied complex systems; and it is not just a score, but it provides an overall scheme of the underlying hierarchy of any directed complex network.
Birds of a feather scam together: Trustworthiness homophily in a business network
Barone, M. & Coscia, M., 2018. Birds of a feather scam together: Trustworthiness homophily in a business network. Social Networks , 54 (July 2018) , pp. 228-237. Publisher's VersionAbstract
Estimating the trustworthiness of a set of actors when all the available information is provided by the actors themselves is a hard problem. When two actors have conflicting reports about each other, how do we establish which of the two (if any) deserves our trust? In this paper, we model this scenario as a network problem: actors are nodes in a network and their reports about each other are the edges of the network. To estimate their trustworthiness levels, we develop an iterative framework which looks at all the reports about each connected actor pair to define its trustworthiness balance. We apply this framework to a customer/supplier business network. We show that our trustworthiness score is a significant predictor of the likelihood a business will pay a fine if audited. We show that the market network is characterized by homophily: businesses tend to connect to partners with similar trustworthiness degrees. This suggests that the topology of the network influences the behavior of the actors composing it, indicating that market regulatory efforts should take into account network theory to prevent further degeneration and failures.
Coscia, M. & Neffke, F., 2017. Network Backboning with Noisy Data. 2017 IEEE 33rd International Conference on Data Engineering (ICDE) , (May) , pp. 425-436. Publisher's VersionAbstract
Networks are powerful instruments to study complex phenomena, but they become hard to analyze in data that contain noise. Network backbones provide a tool to extract the latent structure from noisy networks by pruning non-salient edges. We describe a new approach to extract such backbones. We assume that edge weights are drawn from a binomial distribution, and estimate the error-variance in edge weights using a Bayesian framework. Our approach uses a more realistic null model for the edge weight creation process than prior work. In particular, it simultaneously considers the propensity of nodes to send and receive connections, whereas previous approaches only considered nodes as emitters of edges. We test our model with real world networks of different types (flows, stocks, cooccurrences, directed, undirected) and show that our Noise-Corrected approach returns backbones that outperform other approaches on a number of criteria. Our approach is scalable, able to deal with networks with millions of edges.
The Mobility of Displaced Workers: How the Local Industry Mix Affects Job Search
Neffke, F., Otto, A. & Hidalgo, C., 2018. The Mobility of Displaced Workers: How the Local Industry Mix Affects Job Search. Journal of Urban Economics , 108 (November 2018) , pp. 124-140. Publisher's VersionAbstract
Are there Marshallian externalities in job search? We study how workers who lose their jobs in establishment closures in Germany cope with their loss of employment. About a fifth of these displaced workers do not return to social-security covered employment within the next three years. Among those who do get re-employed, about two-thirds leave their old industry and one-third move out of their region. However, which of these two types of mobility responses workers will choose depends on the local industry mix in ways that are suggestive of Marshallian benefits to job search. In particular, large concentrations of one’s old industry makes it easier to find new jobs: in regions where the pre-displacement industry is large, displaced workers suffer relatively small earnings losses and find new work faster. In contrast, large local industries skill-related to the pre-displacement industry increase earnings losses but also protect against long-term unemployment. Analyzed through the lens of a job-search model, the exact spatial and industrial job-switching patterns reveal that workers take these Marshallian externalities into account when deciding how to allocate search efforts among industries.
The workforce of pioneer plants: The role of worker mobility in the diffusion of industries
Hausmann, R. & Neffke, F., 2018. The workforce of pioneer plants: The role of worker mobility in the diffusion of industries. Research Policy. Publisher's VersionAbstract

Does technology require labour mobility to diffuse? To explore this, we use German social-security data and ask how plants that pioneer an industry in a location – and for which the local labour market offers no experienced workers – assemble their workforces. These pioneers use different recruiting strategies than plants elsewhere: they hire more workers from outside their industry and from outside their region, especially when workers come from closely related industries or are highly skilled. The importance of access to experienced workers is highlighted in the diffusion of industries from western Germany to the post-reunification economy of eastern German. While manufacturing employment declined in most advanced economies, eastern German regions managed to reindustrialise. The pioneers involved in this process relied heavily on expertise from western Germany: while establishing new manufacturing industries in the East, they sourced half of their experienced workers from the West.

Originally published as CID Working Paper 310
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