Frank Neffke

2024

2024. A journey through time: the story behind ‘eight decades of changes in occupational tasks, computerization and the gender pay gap’. Industry and Innovation , 31 (4). Publisher's Version Abstract

In this interview article, we embark on a fascinating journey through time alongside the winners of the 2023 DRUID Best Paper Award. DRUID, an annual research conference renowned as the hub of cutting-edge research on innovation and the dynamics of structural, institutional, and geographic change, bestows this award on the most innovative and exceptional conference submission. As longstanding allies of DRUID, Industry and Innovation offers an exclusive peek behind the curtains, unveiling the untold stories that underlie award-winning research.

In 2023, this coveted DRUID prize was awarded to a paper by Ljubica Nedelkoska, Shreyas Gadgin Matha, James McNerney, Andre Assumpcao, Dario Diodato, and Frank Neffke. Their work stands out through an impressive data collection effort and the exploration of a compelling and urgent research question – how technological change has impacted the gender pay gap. Throughout this interview, the author team takes us down memory lane, retelling the story behind their research project. On this journey through time, we trace the genesis of the authors’ innovative ideas and the intricate pathways they navigated in their quest to understand the past as a means of unravelling the future of work and its implications for gender inequality in the labour market. This journey not only takes us back in time but also points to potential avenues for future research and open questions that lie ahead.

2023

Nedelkoska, L., et al., 2023. Eight Decades of Changes in Occupational Tasks, Computerization and the Gender Pay Gap.Abstract

We build a new longitudinal dataset of job tasks and technologies by transforming the U.S. Dictionary of Occupational Titles (DOT, 1939 -1991) and four books documenting occupational use of tools and technologies in the 1940s, into a database akin to, and comparable with its digital successor, the O*NET (1998 -today). After creating a single occupational classification stretching between 1939 and 2019, we connect all DOT waves and the decennial O*NET databases into a single dataset, and we connect these with the U.S. Decennial Census data at the level of 585 occupational groups. We use the new dataset to study how technology changed the gender pay gap in the United States since the 1940s. We find that computerization had two counteracting effects on the pay gap -it simultaneously reduced it by attracting more women into better-paying occupations, and increased it through higher returns to computer use among men. The first effect closed the pay gap by 3.3 pp, but the second increased it by 5.8 pp, leading to a net widening of the pay gap.

2023-06-cid-fellows-wp-151-occupational-tasks.pdf

Diodato, D., Hausmann, R. & Neffke, F., 2023. The impact of return migration on employment and wages in Mexican cities. Journal of Urban Economics , 135 (May). Publisher's Version Abstract

How does return migration from the US to Mexico affect local workers? Return migrants increase the local labor supply, potentially hurting local workers. However, having been exposed to a more advanced U.S. economy, they may also carry human capital that benefits non-migrants. Using an instrument based on involuntary return migration, we find that, whereas workers who share returnees’ occupations experience a fall in wages, workers in other occupations see their wages rise. These effects are, however, transitory and restricted to the city-industry receiving the returnees. In contrast, returnees permanently alter a city’s long-run industrial composition, by raising employment levels in the local industries that hire them.

Li, Y. & Neffke, F., 2023. Evaluating the Principle of Relatedness: Estimation, Drivers and Implications for Policy.Abstract

A growing body of research documents that the size and growth of an industry in a place depends on how much related activity is found there. This fact is commonly referred to as the "principle of relatedness." However, there is no consensus on why we observe the principle of relatedness, how best to determine which industries are related or how this empirical regularity can help inform local industrial policy. We perform a structured search over tens of thousands of specifications to identify robust – in terms of out-of-sample predictions – ways to determine how well industries fit the local economies of US cities. To do so, we use data that allow us to derive relatedness from observing which industries co-occur in the portfolios of establishments, firms, cities and countries. Different portfolios yield different relatedness matrices, each of which help predict the size and growth of local industries. However, our specification search not only identifes ways to improve the performance of such predictions, but also reveals new facts about the principle of relatedness and important trade-offs between predictive performance and interpretability of relatedness patterns. We use these insights to deepen our theoretical understanding of what underlies path-dependent development in cities and expand existing policy frameworks that rely on inter-industry relatedness analysis.

2023-03-cid-fellows-wp-146-principle-of-relatedness.pdf

2022

Ribeiro, A., Neffke, F. & Hausmann, R., 2022. What Can the Millions of Random Treatments in Nonexperimental Data Reveal About Causes?. SN Computer Science , 3 (6). Publisher's Version Abstract

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 pairs can be combined to provide more accurate estimates of causal effects in nonexperimental data, provided a statistical model relating 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(n2) pairwise observations typically available in nonexperimental 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. As a tool, the approach also allows researchers to represent and visualize possible causes, and heterogeneous subpopulations, in their samples.

Neffke, F., Hartog, M. & Li, Y., 2022. The Economic Geography of the War in Ukraine. Complexity Science Hub Vienna.Abstract

The war in Ukraine has been waging for a month now, not only causing human suffering on a massive scale, but also sending economic tremors that are felt far beyond the country’s borders. Since the collapse of the Soviet Union, Ukraine’s economy has been pulled between its strong historical ties with the Russian economy and the opportunities in forging new ties with the European Union (EU). With the help of Metroverse, an online tool for analyzing the local economies of over a thousand cities worldwide, and of the data that power this tool, we analyze the evolving economic relations between Ukraine, Russia and the West and weigh the consequences of their disruption.

Explore: The Economic Geography of the War in Ukraine

2021

McNerney, J., et al., 2021. Bridging the short-term and long-term dynamics of economic structural change.Abstract

In the short-term, economies shift preferentially into new activities that are related to ones they currently do. Such a tendency should have implications for the nature of an economy’s long-term development as well. We explore these implications using a dynamical network model of an economy’s movement into new activities. First, we theoretically derive a pair of coordinates that summarize long-term structural change. One coordinate captures overall ability across activities, the other captures an economy’s composition. Second, we show empirically how these two measures intuitively summarize a variety of facts of long-term economic development. Third, we observe that our measures resemble complexity metrics, though our route to these metrics differs significantly from previous ones. In total, our framework represents a dynamical approach that bridges short-and long-term descriptions of structural change, and suggests how different branches of economic complexity analysis could potentially fit together in one framework.

2021-10-cid-wp-133-bridging-dynamics-of-economic-structural-change.pdf

2020

Coscia, M., et al., 2020. The Node Vector Distance Problem in Complex Networks. ACM Computing Surveys , 53 (6). Publisher's Version Abstract

We describe a problem in complex networks we call the Node Vector Distance (NVD) problem, and we survey algorithms currently able to address it. Complex networks are a useful tool to map a non-trivial set of relationships among connected entities, or nodes. An agent—e.g., a disease—can occupy multiple nodes at the same time and can spread through the edges. The node vector distance problem is to estimate the distance traveled by the agent between two moments in time. This is closely related to the Optimal Transportation Problem (OTP), which has received attention in fields such as computer vision. OTP solutions can be used to solve the node vector distance problem, but they are not the only valid approaches. Here, we examine four classes of solutions, showing their differences and similarities both on synthetic networks and real world network data. The NVD problem has a much wider applicability than computer vision, being related to problems in economics, epidemiology, viral marketing, and sociology, to cite a few. We show how solutions to the NVD problem have a wide range of applications, and we provide a roadmap to general and computationally tractable solutions. We have implemented all methods presented in this article in a publicly available open source library, which can be used for result replication.

Hartog, M., Lopez-Cordova, J.E. & Neffke, F., 2020. Assessing Ukraine's Role in European Value Chains: A Gravity Equation-cum-Economic Complexity Analysis Approach.Abstract

We analyze Ukraine's opportunities to participate in European value chains, using traditional gravity models, combined with tools from Economic Complexity Analysis to study international trade (exports) and Foreign Direct Investment (FDI). This toolbox is shown to be predictive of the growth and entry of new exports to the EU's Single Market, as well as foreign direct investments from the Single Market in Ukraine. We find that Ukraine has suffered from a decline of trade with Russia, which has led not only to a quantitative but also a qualitative deterioration in Ukrainian exports. Connecting to western European value chains is in principle possible, with several opportunities in the automotive, information technology and other sectors. However, such a shift may lead to a spatial restructuring of the Ukrainian economy and a mismatch between the geographical supply of and demand for labor.

2020-10-cid-fellows-wp-129-ukraine-role.pdf

Coscia, M., Neffke, F. & Hausmann, R., 2020. Knowledge Diffusion in the Network of International Business Travel. Nature Human Behaviour , 4 (10). Publisher's Version Abstract

We use aggregated and anonymized information based on international expenditures through corporate payment cards to map the network of global business travel. We combine this network with information on the industrial composition and export baskets of national economies. The business travel network helps to predict which economic activities will grow in a country, which new activities will develop and which old activities will be abandoned. In statistical terms, business travel has the most substantial impact among a range of bilateral relationships between countries, such as trade, foreign direct investments and migration. Moreover, our analysis suggests that this impact is causal: business travel from countries specializing in a specific industry causes growth in that economic activity in the destination country. Our interpretation of this is that business travel helps to diffuse knowledge, and we use our estimates to assess which countries contribute or benefit the most from the diffusion of knowledge through global business travel.

Additional content:

2019

Neffke, F., 2019. The Value of Complementary Coworkers. Science Advances , 5 (12). Publisher's Version Abstract

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?”

Additional resources: Website | Podcast | Video | Media Release

Nedelkoska, L. & Neffke, F., 2019. Skill Mismatch and Skill Transferability: Review of Concepts and Measurements. Papers in Evolutionary Economic Geography , 19 (21). Publisher's Version Abstract

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.

2018

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 Version Abstract

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.

Hausmann, R. & Neffke, F., 2018. The workforce of pioneer plants: The role of worker mobility in the diffusion of industries. Research Policy. Publisher's Version Abstract

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

Nedelkoska, L., Diodato, D. & Neffke, F., 2018. Is Our Human Capital General Enough to Withstand the Current Wave of Technological Change?.Abstract

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.

humancapital_automation_cidrfwp93.pdf

Diodato, D., Neffke, F. & O'Clery, N., 2018. Why do Industries Coagglomerate? How Marshallian Externalities Differ by Industry and Have Evolved Over Time.Abstract

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.

Original version of this paper was published in 2016.

cidrfwp89.pdf

Neffke, F., et al., 2018. Agents of Structural Change: The Role of Firms and Entrepreneurs in Regional Diversification. Economic Geography , pp. 1-26. Publisher's Version Abstract

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.

2017

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 Version Abstract

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.

Neffke, F., Otto, A. & Weyh, A., 2017. Inter-industry Labor Flows. Journal of Economic Behavior and Organization , 142 (October) , pp. 275-292. Publisher's Version Abstract

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.

inter_industry_labor_jebo.pdf

Neffke, F., 2017. Coworker complementarity.Abstract

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.

rfwp79_neffke.pdf

Frank Neffke

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