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.
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.
The degree to which modern technologies are able to substitute for groups of job tasks has renewed fears of near-future technological unemployment. We argue that our knowledge, skills and abilities (KSA) go beyond the specific tasks we do at the job, making us potentially more adaptable to technological change than feared. The disruptiveness of new technologies depends on the relationships between the job tasks susceptible to automation and our KSA. Here we first demonstrate that KSA are general human capital features while job tasks are not, suggesting that human capital is more transferrable across occupations than what job tasks would predict. In spite of this, we document a worrying pattern where automation is not randomly distributed across the KSA space – it is concentrated among occupations that share similar KSA. As a result, workers in these occupations are making longer skill transitions when changing occupations and have higher probability of unemployment.
The fact that firms benefit from close proximity to other firms with which they can exchange inputs, skilled labor or know-how helps explain why many industrial clusters are so successful. Studying the evolution of coagglomeration patterns, we show that which type of agglomeration benefits firms has drastically changed over the course of a century and differs markedly across industries. Whereas, at the beginning of the twentieth century, industries tended to colocate with their value chain partners, in more recent decades the importance of this channels has declined and colocation seems to be driven more by similarities industries' skill requirements. By calculating industry-specific Marshallian agglomeration forces, we are able to show that, nowadays, skill-sharing is the most salient motive in location choices of services, whereas value chain linkages still explain much of the colocation patterns in manufacturing. Moreover, the estimated degrees to which labor and input-output linkages are reflected in an industry's coagglomeration patterns help improve predictions of city-industry employment growth.
Who introduces structural change in regional economies: Entrepreneurs or existing firms? And do local or nonlocal establishment founders create most novelty in a region? We develop a theoretical framework that focuses on the roles different agents play in regional transformation. We then apply this framework, using Swedish matched employer–employee data, to determine how novel the activities of new establishments are to a region. Incumbents mainly reinforce a region’s current specialization: incumbent’s growth, decline, and industry switching further align them with the rest of the local economy. The unrelated diversification required for structural change mostly originates via new establishments, especially via those with nonlocal roots. Interestingly, although entrepreneurs often introduce novel activities to a local economy, when they do so, their ventures have higher failure rates compared to new subsidiaries of existing firms. Consequently, new subsidiaries manage to create longer-lasting change in regions.
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.
Using German social security data, we study inter-industry labor mobility to assess how industry-specific human capital is and to determine which industries have similar human capital requirements. We find that inter-industry labor flows are highly concentrated in just a handful of industry pairs. Consequently, labor flows connect industries in a sparse network. We interpret this network as an expression of industries similarities in human capital requirements, or skill relatedness. This skill-relatedness network is stable over time, similar for different types of workers and independent of whether workers switch jobs locally or over larger distances. Moreover, in an application to regional diversification and local industry growth, skill relatedness proves to be more predictive than colocation or value chain relations. To facilitate future research, we make detailed inter-industry relatedness matrices online available.
How important is working with people who complement one's skills? Using administrative data that record which of 491 educational tracks each worker in Sweden absolved, I quantify the educational fit among coworkers along two dimensions: coworker match and coworker substitutability. Complementary coworkers raise wages with a comparable factor as does a college degree, whereas working with close substitutes is associated with wage penalties. Moreover, this coworker fit does not only account for large portions of the urban and large-plant wage premiums, but the returns to own schooling and the urban wage premium are almost completely contingent on finding complementary coworkers.
Tourism is one of the most important economic activities in the world: for many countries it represents the single largest product in their export basket. However, it is a product difficult to chart: "exporters" of tourism do not ship it abroad, but they welcome importers inside the country. Current research uses social accounting matrices and general equilibrium models, but the standard industry classifications they use make it hard to identify which domestic industries cater to foreign visitors. In this paper, we make use of open source data and of anonymized and aggregated transaction data giving us insights about the spend behavior of foreigners inside two countries, Colombia and the Netherlands, to inform our research. With this data, we are able to describe what constitutes the tourism sector, and to map the most attractive destinations for visitors. In particular, we find that countries might observe different geographical tourists' patterns - concentration versus decentralization -; we show the importance of distance, a country's reported wealth and cultural affinity in informing tourism; and we show the potential of combining open source data and anonymized and aggregated transaction data on foreign spend patterns in gaining insight as to the evolution of tourism from one year to another.
We document the heterogeneity across sectors in the impact labor and input-output links have on industry agglomeration. Exploiting the available degrees of freedom in coagglomeration patterns, we estimate the industry-specic benefits of sharing labor needs and supply links with local firms. On aggregate, coagglomeration patterns of services are at least as strongly driven by input-output linkages as those of manufacturing, whereas labor linkages are much more potent drivers of coagglomeration in services than in manufacturing. Moreover, the degree to which labor and input-output linkages are reflected in an industry's coagglomeration patterns is relevant for predicting patterns of city-industry employment growth.
Labor flows across industries reallocate resources and diffuse knowledge among economic activities. However, surprisingly little is known about the structure of such inter-industry flows. How freely do workers switch jobs among industries? Between which pairs of industries do we observe such switches? Do different types of workers have different transition matrices? Do these matrices change over time?
Using German social security data, we generate stylized facts about inter-industry labor mobility and explore its consequences. We find that workers switch industries along tight paths that link industries in a sparse network. This labor-flow network is relatively stable over time, similar for workers in different occupations and wage categories and independent of whether workers move locally or over larger distances. When using these networks to construct inter-industry relatedness measures they prove better predictors of local industry growth rates than co-location or input-based alternatives. However, because industries that exchange much labor typically do not have correlated growth paths, the sparseness of the labor-flow network does not necessarily prevent a smooth reallocation of workers from shrinking to growing industries. To facilitate future research, the inter-industry relatedness matrices we develop are made available as an online appendix to this paper.
Establishment closures leave many workers unemployed. Based on employment histories of 20 million German workers, we find that workers often cope with their displacement by moving to different regions and industries. However, which of these coping strategies is chosen depends on the local industry mix. A large local presence of predisplacement or related industries strongly reduces the rate at which workers leave the region. Moreover, our findings suggest that a large local presence of the predisplacement industry induces workers to shift search efforts toward this industry, reducing the spatial scope of search for jobs in alternative industries and vice versa.
Is labor mobility important in technological diffusion? We address this question by asking how plants assemble their workforce if they are industry pioneers in a location. By definition, these plants cannot hire local workers with industry experience. Using German social-security data, we find that such plants recruit workers from related industries from more distant regions and local workers from less-related industries. We also show that pioneers leverage a low-cost advantage in unskilled labor to compete with plants that are located in areas where the industry is more prevalent. Finally, whereas research on German reunification has often focused on the effects of east-west migration, we show that the opposite migration facilitated the industrial diversification of eastern Germany by giving access to experienced workers from western Germany.
In this document we describe the size of the Poblacion Flotante of Bogota (D.C.). The Poblacion Flotante is composed by people who live outside Bogota (D.C.), but who rely on the city for performing their job. We estimate the Poblacion Flotante impact relying on a new data source provided by telecommunications operators in Colombia, which enables us to estimate how many people commute daily from every municipality of Colombia to a specic area of Bogota (D.C.). We estimate that the size of the Poblacion Flotante could represent a 5.4% increase of Bogota (D.C.)'s population. During weekdays, the commuters tend to visit the city center more.
Who introduces structural change in regional economies: Entrepreneurs or existing firms? And do local or non‐local founders of establishments create most novelty in a region? Using matched employer/employee data for the whole Swedish workforce, we determine how unrelated and therefore how novel the activities of different establishments are to a region’s industry mix. Up‐ and downsizing establishments cause large shifts in the local industry structure, but these shifts only occasionally require an expansion of local capabilities because the new activities are often related to existing local activities. Indeed, these incumbents tend to align their production with the local economy, deepening the region’s specialization. In contrast, structural change mostly originates via new establishments, especially those with non‐local roots. Moreover, although entrepreneurs start businesses more often in activities unrelated to the existing regional economy, new establishments founded by existing firms survive in such activities more often, inducing longer‐lasting changes in the region.