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The uniqueness of human labour is at question in times of smart technologies. As computing power and data available increases, the discussion on technological unemployment reawakens. Prominently, Frey and Osborne (Technol Forecast Soc Change 114:254–280, 2017) estimated that half of US employment must be considered exposed to computerization within the next 20 years; followed by a series of papers expanding the research with information on heterogeneous job-specific tasks within the same jobs diminishing digitization potentials to only smaller fractions of workers at high risk. The main contribution of our work is to show that the diversity of previous findings regarding the degree of digitization is additionally driven by model selection. For our case study, we consult experts in machine learning and industry professionals on the susceptibility to digital technologies in the Austrian labour market. Our results indicate that, while clerical computer-based routine jobs are likely to change in the next decade, professional activities, such as the processing of complex information, are less prone to digital change.

Startup companies solve many of today’s most complex and challenging scientific, technical and social problems, such as the decarbonisation of the economy, air pollution, and the development of novel life-saving vaccines. Startups are a vital source of social, scientific and economic innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors as well as the team’s size. The effects of founders’ personalities on the success of new ventures are mainly unknown. Here we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n=26,781). We found that the Big 5 personality traits of startup founders across 30 dimensions significantly differed from that of the population at large. We can train a classifier to distinguish founders from employees with 82.5% accuracy. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). However, we do not find one “Founder-type” personality; instead, six different personality types appear, with startups founded by a “Hipster, Hacker and Hustler” being twice as likely to succeed. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which has the potential to be extended through further research into other team settings within business, government and research.

The global spread of Covid-19 has caused major economic disruptions. Governments around the world provide considerable financial support to mitigate the economic downturn. However, effective policy responses require reliable data on the economic consequences of the corona pandemic. We propose the CoRisk-Index: a real-time economic indicator of Covid-19 related risk assessments by industry. Using data mining, we analyse all reports from US companies filed since January 2020, representing more than a third of all US employees. We construct two measures - the number of 'corona' words in each report and the average text negativity of the sentences mentioning corona in each industry - that are aggregated in the CoRisk-Index. The index correlates with U.S. unemployment data and preempts stock market losses of February 2020. Moreover, thanks to topic modelling and natural language processing techniques, the CoRisk data provides unique granularity with regards to the particular contexts of the crisis and the concerns of individual industries about them. The data presented here help researchers and decision makers to measure, the previously unobserved, risk awareness of industries with regard to Covid-19, bridging the quantification gap between highly volatile stock market dynamics and long-term macro-economic figures. For immediate access to the data, we provide all findings and raw data on an interactive online dashboard in real time.

The uniqueness of human labour is at question in times of smart technologies. The 250 years-old discussion on technological unemployment reawakens. Prominently, Frey and Osborne (2017) estimated that half of US employment will be automated by algorithms within the next 20 years. Other follow-up studies conclude that only a small fraction of workers will be replaced by digital technologies. The main contribution of our work is to show that the diversity of previous findings regarding the degree of job automation is, to a large extent, driven by model selection and not by controlling for personal characteristics or tasks. For our case study, we consult experts in machine learning and industry professionals on the susceptibility to digital technologies in the Austrian labour market. Our results indicate that, while clerical computer-based routine jobs are likely to change in the next decade, professional activities, such as the processing of complex information, are less prone to digital change.

The Covid-19 pandemic has led to the rise of remote work with consequences for the global division of work. Remote work could connect labour markets, but it could also increase spatial polarisation. However, our understanding of the geographies of remote work is limited. Specifically, does remote work bring jobs to rural areas or is it concentrating in large cities, and how do skill requirements affect competition for jobs and wages? We use data from a fully remote labour market - an online labour platform - to show that remote work is polarised along three dimensions. First, countries are globally divided: North American, European, and South Asian remote workers attract most jobs, while many Global South countries participate only marginally. Secondly, remote jobs are pulled to urban regions; rural areas fall behind. Thirdly, remote work is polarised along the skill axis: workers with in-demand skills attract profitable jobs, while others face intense competition and obtain low wages. The findings suggest that remote work is shaped by agglomerative forces, which are deepening the gap between urban and rural areas. To make remote work an effective tool for rural development, it needs to be embedded in local skill-building and labour market programmes.

Digital technologies are radically transforming our work environments and demand for skills, with certain jobs being automated away and others demanding mastery of new digital techniques. This global challenge of rapidly changing skill requirements due to task automation overwhelms workers. The digital skill gap widens further as technological and social transformation outpaces national education systems and precise skill requirements for mastering emerging technologies, such as Artificial Intelligence, remain opaque. Online labour platforms could help us to understand this grand challenge of reskilling en masse. Online labour platforms build a globally integrated market that mediates between millions of buyers and sellers of remotely deliverable cognitive work. This commentary argues that, over the last decade, online labour platforms have become the ‘laboratories’ of skill rebundling; the combination of skills from different occupational domains. Online labour platform data allows us to establish a new taxonomy on the individual complementarity of skills. For policy makers, education providers and recruiters, a continuous analysis of complementary reskilling trajectories enables automated, individual and far-sighted suggestions on the value of learning a new skill in a future of technological disruption.

This paper examines whether and how climatic shocks influence individual migration decisions. We use census microdata across 64 countries over the period 1960 to 2012, covering 442 million individual records, combined with geo-referenced temperature and precipitation data summarised for each origin and destination administrative unit. Migration is identified when an individual changed a place of usual residence one, five or ten years ago to a new major administrative unit in the same country. Given an exceptionally large number of observations, we apply a two-step approach to analyse the relationship between exposure to climatic shocks and migration. First, we use random forest models to uncover that in many countries climatic shocks are as important as better-known individual-level covariates in determining migration decisions. This observation serves as a yardstick for the second step of our analysis. For a subset of countries, where rainfall shocks play an important role in migration, we compare internal migration patterns across time by examining whether a region experiencing positive or negative rainfall shocks observed higher or lower migration. We find that negative rainfall shocks suppress outmigration particularly for low-income countries. The opposite is true for positive rainfall shocks whereby migration is found to increase, especially for lower-income countries. Our finding supports the liquidity constraint argument whereby adverse climatic conditions can disrupt migration financing and consequently suppress ability to migrate.

An unknown number of people around the world are earning income by working through online labour platforms such as Upwork and Amazon Mechanical Turk. We combine data collected from various sources to build a data-driven assessment of the number of such online workers (also known as online freelancers) globally. Our headline estimate is that there are 163 million freelancer profiles registered on online labour platforms globally. Approximately 19 million of them have obtained work through the platform at least once, and 5 million have completed at least 10 projects or earned at least $1000. These numbers suggest a substantial growth from 2015 in registered worker accounts, but much less growth in amount of work completed by workers. Our results indicate that online freelancing represents a non-trivial segment of labour today, but one that is spread thinly across countries and sectors., Comment: 16 pages, four figures, two tables

Twitter influences political debates. Phenomena like fake news and hate speech show that political discourse on micro-blogging can become strongly polarised by algorithmic enforcement of selective perception. Some political actors actively employ strategies to facilitate polarisation on Twitter, as past contributions show, via strategies of 'hashjacking'. For the example of COVID-19 related hashtags and their retweet networks, we examine the case of partisan accounts of the German far-right party Alternative f\"ur Deutschland (AfD) and their potential use of 'hashjacking' in May 2020. Our findings indicate that polarisation of political party hashtags has not changed significantly in the last two years. We see that right-wing partisans are actively and effectively polarising the discourse by 'hashjacking' COVID-19 related hashtags, like #CoronaVirusDE or #FlattenTheCurve. This polarisation strategy is dominated by the activity of a limited set of heavy users. The results underline the necessity to understand the dynamics of discourse polarisation, as an active political communication strategy of the far-right, by only a handful of very active accounts., Comment: 10 pages, 3 figures

Fabian Stephany on the CoRisk-Index, its development during the course of the COVID-19 pandemic and the nexus between implicit and explicit theory.

This work proposes a network perspective in order to empirically identify the relevant ICT skills related to AI, to what extent they are systemically related, and how their composition varies across regions. With the example of 5,227 job openings from Germany advertised as postings in Artificial Intelligence, relevant skills are identified and connected in a network fashion. Two skills are connected, if they are jointly required by the same job advertisement. Similarly, regional skill networks can be constructed: Job postings are screened by city location and skill networks are constructed for this set of regional postings exclusively. The resulting networks depict the regional city ecosystem of AI skills currently in demand.

Election prediction has long been an evergreen in political science literature. Traditionally, such efforts included polling aggregates, economic indicators, partisan affiliation, and campaign effects to predict aggregate voting outcomes. With increasing secondary usage of online-generated data in social science, researchers have begun to consult metadata from widely used web-based platforms such as Facebook, Twitter, Google Trends and Wikipedia to calibrate forecasting models. Web-based platforms offer the means for voters to retrieve detailed campaign-related information, and for researchers to study the popularity of campaigns and public sentiment surrounding them. However, past contributions have often overlooked the interaction between conventional election variables and information-seeking behaviour patterns. In this work, we aim to unify traditional and novel methodology by considering how information retrieval differs between incumbent and challenger campaigns, as well as the effect of perceived candidate viability and media coverage on Wikipedia pageviews predictive ability. In order to test our hypotheses, we use election data from United States Congressional (Senate and House) elections between 2016 and 2018. We demonstrate that Wikipedia data, as a proxy for information-seeking behaviour patterns, is particularly useful for predicting the success of well-funded challengers who are relatively less covered in the media. In general, our findings underline the importance of a mixed-data approach to predictive analytics in computational social science., Comment: 24 pages

In the digital economy, innovation processes increasingly rely on highly specialised know-how and open-source software shared on digital platforms on collaborative programming. The information that feeds into the content on these platforms is provided voluntarily by a vast crowd of knowledgeable users from all over the world. In contributing to the platforms, users invest their time and share knowledge with strangers to add to the rising body of digital knowledge.This requires an open mindset and trust. In this study, we argue that such a mindset is not just an individual asset, but determined by the local communities the users are embedded in. We, therefore, hypothesise that places with higher levels of trust should contribute more to StackOverflow, the world’s largest question-and-answer platform for programming questions. In relating the city-level contributions of 266 OECD metropolitan areas to infrastructure, economic, and trust measures, we find this hypothesis confirmed. In contrast, click rates to the platform are solely driven by infrastructure and economic variables, but not by trust. These findings highlight the importance of societal values in the 21st century knowledge economy: if policy-makers want to develop a lively local digital economy, it is not enough to provide fast Internet access and business opportunities. Instead, it is equally important to establish a trust-building environment that fosters sharing of innovative ideas, collaborations, and knowledge spillovers.

This work examines the economic benefits of learning a new skill from a different domain: cross-skilling. To assess this, a network of skills from the job profiles of 14,790 online freelancers is constructed. Based on this skill network, relationships between 3,480 different skills are revealed and marginal effects of learning a new skill can be calculated via workers’ wages. The results indicate that learning in-demand skills, such as popular programming languages, is beneficial in general, and that diverse skill sets tend to be profitable, too. However, the economic benefit of a new skill is individual, as it complements the existing skill bundle of each worker. As technological and social transformation is reshuffling jobs’ task profiles at a fast pace, the findings of this study help to clarify skill sets required for designing individual re-skilling pathways. This can help to increase employability and reduce labour market shortages.

While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such policy measures, it is key to identify the industries that are most in need of support. In this study, we introduce a data-mining approach to measure industry-specific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries. The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is updated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis.

Digital platforms offer vast potential for increased value creation and innovation, especially through cross-organizational data sharing. It appears that SMEs in Germany are currently hesitant or unable to create their own platforms. To get a holistic overview of the structure of the German SME-focused platform landscape (that is platforms that are led by or targeting SMEs), we applied a mixed method approach of traditional desk research and a quantitative analysis. The study identified large geographical disparity along the borders of the new and old German federal states, and overall fewer platform ventures by SMEs, rather than large companies and startups. Platform ventures for SMEs are more likely set up as partnerships. We indicate that high capital intensity might be a reason for that.

In every social transaction there is an element of trust. The degree to which we trust others, called generalized trust, is assumed to benefit from interaction with different social groups. In the trust literature, it is opposed by particularized trust, which represents our mutual confidence in individuals close to us, for example, family members and friends. This study, based on a survey with 634 university students from Austria, questions the strict dichotomy between the two trust types. Our results advocate for a third, group determined type of trust. This additional trust dimension is measured by the number of groups individuals participate in. It changes fluently between particularized and generalized trust, depending on measures of group context, like frequency of interaction or group size. Our findings show that generalized trust increases with the number of groups one feels belonging to. People with less diverse social interaction, however, have more trust in their peers than in strangers.

The prospects for the next Generation - whether young people, regardless of their backgrounds, have equal chances of social success - pose a momentous problem for modern societies. Inequality of opportunity, often reflected by social immobility, is a threat to the egalitarian promise and the stability of your society. This work argues that social capital transmission plays an important role for the chances of social success in Western societies. For the example of Germany, it is reasoned that weak social capital environments deepen existing disadvantages. Even though all levels of education are easily accessible and affordable, Germany has one of the lowest levels of educational mobility among the industrialized countries of the world. Problems appear to be systematic, since the decision regarding entry into higher secondary education is made at early age and is left, in most cases, with the parents, who rely on their own educational trajectory. Outside of the school environment, differences in social capital inheritance explain educational immobility. With the use of the German Socio-Economic Panel survey from 1984 to 2014, various analyses about the relation between social capital and educational success are performed. Social capital, which is helpful for educational and social success, clearly depends on the educational family background. This indirect link has been disregarded in past contributions. Alternative forms of schooling, such as comprehensive and all-day education, as well as a delay of the decision regarding entry into higher education, could help improving unequal social capital inheritance.

Trust is a good approach to explain the functioning of markets, institutions or society as a whole. It is a key element in almost every commercial transaction over time and might be one of the main explanations of economic success and development. Trust diminishes the more we perceive others to have economically different living realities. In most of the relevant contributions, scholars have taken a macro perspective on the inequality-trust linkage, with an aggregation of both trust and inequality on a country level. However, patterns of within-country inequality and possibly influential determinants, such as perception and socioeconomic reference, remained undetected. This paper offers the opportunity to look at the interplay between inequality and trust at a more refined level. A measure of (generalized) trust emerges from ESS 5 survey which asks "...generally speaking, would you say that most people can be trusted, or that you can't be too careful in dealing with people?". With the use of 2009 EU-SILC data, measurements of income inequality are developed for age-specific groups of society in 22 countries. A sizable variation in inequality measures can be noticed. Even in low inequality countries, like Sweden, income imbalances within certain age groups have the potential to undermine social trust.

Firms are an important driver of socio-technical transitions, where a technological innovation moves from a protected niche to a new dominant socio-technical regime. The firm's business model functions as an important mediator during this process, articulating a shared vision of what the technology can do. Following recent calls for a closer examination of the business model’s role during socio-technical transitions, we study 375 startups that develop applied AI technologies. Based on a large-scale semantic analysis of the startup’s website texts, we ran a statistical hierarchical clustering and identified three distinct roles: (1) expansion of the technology in scale and scope, (2) exploration of the technology in-depth, (3) new ways of interacting with technology.

Understanding who migrates is crucial in explaining societal changes and forecasting future population composition and size. However, there is no empirical consensus on demographic and socioeconomic factors driving migration decision. Exploiting micro census data from the Integrated Public Use Microdata Series International (IPUMSI) database across 65 countries over the period 1960 to 2012 covering 477,296,432 individual records, this study aims to establish common demographic drivers of migration. Given an exceptionally large number of observations, a parametric approach would simply yield bias estimates of standard errors of the variables of interest. We apply a machine learning technique using decision tree models to establish common demographic patterns driving migration in our data. The decision trees are applied to each country year sample individually in order to control for local optima. Resulting feature selections are compared across countries and years. We find that globally, age, education, household size, and urbanisation are important drivers of internal migration. Age and education are particularly important predictors in Europe and Northern America whilst in South and Central America and Africa, urbanisation and household size are more relevant. The applied method of localised decision trees could be a helpful tool for analysing large-scale census data in other social science domains.

Trust explains the functioning of markets, institutions or society as a whole. It is a key element in almost every commercial transaction over time and might be one of the main explanations of economic success and development. In Europe, the determinants of (generalized) trust have been investigated in the past. Most scholars have focused on aggregate (national) levels of trust. However, it can be assumed that driving forces, which foster or diminish trust, act at a sub-national level. Regional clusters remain undetected. With the use of the European Social Survey 6 and modern spatial diagnostics, this work examines the individual and regional determinants of trust in 88 European NUTS1 regions in 26 countries. There are two main findings. First, wealth, linguistic fragmentation, and religious ideologies shape trust on a regional level, education, income, and membership in associations foster trust on an individual level. Secondly, the study unravels regional dispersions in different types of "trust regimes" in Europe. Regional clusters of generalized trust are confirmed by spatial diagnostics. The "regionality" of trust could be of importance for future targeted policy making.

User data fuel the digital economy, while individual privacy is at stake. Governments react differently to this challenge. Estonia, a small Baltic state, has become a role model for the renewal of the social contract in times of big data (hence, often ironically referred to as "E-stonia"). While e-governance usage has been growing in many parts of Europe during the last ten years, some regions are lagging behind. The Estonian example suggests that online governance is most accepted in a small state, with a young population, trustworthy institutions and the need of technological renewal. This work examines the development of e-governance usage (citizens interacting digitally with the government) during the last decade in Europe from a comprehensive cross-country perspective: Size, age and trust are relevant for the usage of digital government services in Europe. However, the quality of past communication infrastructure is not related to e-governance popularity.

Aiming to explain the European divide with respect to social and political values, scholars in the past have relied on a simplified four- (or even two-) dimensional regime model which tranches the continent according to the social capacities of its inhabitants. This "cartography" of "Social Europe" proves to be outdated by the results presented in this study which re-measures the social capital landscape in Europe. In this work, we apply a factor analysis model to the most commonly used approximations of social capital on the European Social Survey.In addition, we explore, as a novelty in social capital literature, a classification tree to model generalized trust. The analysis shows that three distinct dimensions of social capital measures are important in Europe: additionally to generalised social capital, which is usually approximated by generalised trust, there is one dimension of civic engagement and one of communitarian values. This distinction leads to a new social landscape of Europe, which highlights the relevance of considering regional and cross-border clusters in all relevant social capital dimensions.The results of the non-parametric model reveal that Protestantism and education are good benchmarks to classify trust on an individual level. Based on these findings we argue for the necessity of policies with a regional focus that take the different sub-national structures of social capacity in Europe into account. We re-measure the European Social Capital landscape using current data and provide a novel non-parametrical statistical method from data science for this purpose.

User data fuel the digital economy, while individual privacy is at stake. Governments react differently to this challenge. Estonia, a small Baltic state, has become a role model for the renewal of the social contract in times of big data. While e-governance usage has been growing in many parts of Europe during the last ten years, some regions are lagging behind. The Estonian example suggests that online governance is most accepted in a small state, with a young population, trustworthy institutions and the need of technological renewal. This work examines the development of e-governance usage (citizens interacting digitally with the government) during the last decade in Europe from a comprehensive cross-country perspective: size, age and trust are relevant for the usage of digital government services in Europe. However, the quality of past communication infrastructure is not related to e-governance popularity.