Person:

Neffke, Frank

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 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 significant impact among a range of bilateral relations 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 diffuse knowledge and we use our estimates to assess which countries contribute or benefit most from the knowledge diffusion through global business travel.

Economic development is a path-dependent process in which countries accumulate capabilities that allow them to move into more complex products and industries. Inspired by a theory of capabilities that explains which countries produce which products, these diversification dynamics have been studied in great detail in the literature on economic complexity analysis. However, so far, these capabilities have remained latent and inference is drawn from product spaces that reflect economic outcomes: which products are often exported in tandem. Borrowing a metaphor from biology, such analysis remains phenotypic in nature. In this paper we develop a methodology that allows economic complexity analysis to use capabilities directly. To do so, we interpret the capability requirements of industries as a genetic code that shows how capabilities map onto products. We apply this framework to construct a genotypic product space and to infer countries’ capability bases. These constructs can be used to determine which capabilities a country would still need to acquire if it were to diversify into a given industry. We show that this information is not just valuable in predicting future diversification paths and to advance our understanding of economic development, but also to design more concrete policy interventions that go beyond targeting products by identifying the underlying capability requirements.

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.

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.

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 tracks 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.

We introduce a record linkage algorithm that allows one to (1) efficiently match hundreds of millions of records based not just on demographic characteristics but also name similarity, (2) make statistical choices regarding the trade-off between match quality and representativity and (3) automatically generate a ground truth of true and false matches, suitable for training purposes, based on networked family relationships. Given the recent availability of hundreds of millions of digitized census records, this algorithm significantly reduces computational costs to researchers while allowing them to tailor their matching design towards their research question at hand (e.g. prioritizing external validity over match quality). Applied to U.S Census Records from 1850 to 1940, the algorithm produces two sets of matches, one designed for representativity and one designed to maximize the number of matched individuals. At the same level of accuracy as commonly used methods, the algorithm tends to have a higher level of representativity and a larger pool of matches. The algorithm also allows one to match harder-to-match groups with less bias (e.g. women whose names tend to change over time due to marriage).

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.

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.

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.

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.